Experimental Analysis of Image Dehazing Algorithms for

ISIJ International, Vol. 61 (2021), No. 1

© 2021 ISIJ1

ISIJ International, Vol. 61 (2021), No. 1, pp. 1–11

https://doi.org/10.2355/isijinternational.ISIJINT-2020-295

* Corresponding author: E-mail: [email protected]

© 2021 The Iron and Steel Institute of Japan. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs license (https://creativecommons.org/licenses/by-nc-nd/4.0/).CCBYNCND

1. Introduction

Pelletization is a size-enlargement process to mold pow-der particles into the shape of pellets, which is a common process used in metallurgical and chemical industries.1) Figure 1 gives an example of pelletization process in rotary disc equipment in steel industry. The hybrid raw material of iron ore fines, bentonite, and other powders are conveyed into a rotary disc and sprayed from above. As the disc rotates, tiny particles gather into larger pellets that are even-tually expelled from the disc as a product (so called “green pellets”). Pellet size distribution is an important quality con-trol index, because it greatly affects the energy consumption and producing efficiency in the following sintering process in blast furnace.1–3)

In recent years, machine vision has become a popular method for online monitoring the size distribution of green

Experimental Analysis of Image Dehazing Algorithms for Pelletization Process Images

Xin WU,1,2) Xiaoyan LIU1,3)* and Fei YUAN1)

1) College of Electrical and Information Engineering, Hunan University, Changsha, 410082 China.2) National Engineering Laboratory for Robot Visual Perception and Control Technology, Changsha, 410082 China.3) Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing, Changsha, 410082 China.

(Received on May 15, 2020; accepted on July 10, 2020; J-STAGE Advance published date: November 11, 2020)

The product quality of pelletization process in steel industry is usually monitored by machine vision system. However, the image quality deteriorates significantly by haze generated during pelletization. Cur-rent image dehazing algorithms mainly concentrate on natural haze in outdoor or synthetic hazy images. Whether these algorithms can be directly adopted in solving haze removal problem in industrial process images, needs to be studied. In the present work, experiments are performed to compare the perfor-mance of five state-of-the-art image dehazing algorithms, using the image dataset PELLET that consists of real hazy images captured from pelletization process in a local steel company. For a comprehensive comparative study of the image dehazing algorithms, both qualitative and quantitative evaluation criteria are adopted, including visual perceptual evaluation, no-reference image quality assessment, and task-driven comparison. Our experimental analysis demonstrates that Boundary Constrained Context Regularization (BCCR) and Non-local (NLD) image dehazing algorithms generally achieve better quality of restored image than the other three algorithms (Dark-Channel Prior, Optimized Contrast Enhancement, and AOD deep learning network) in dealing with pelletization process images with different haze levels. The computing time needed by BCCR algorithm is only half of that by NLD (2.5 vs. 5 seconds) in processing a hazy image of size 656 × 490. Nevertheless, the performance of these algorithms needs to be improved in the future to deal with pelletization process images with dense haze, as well as to meet with the real-time requirement of pelletization process monitoring.

KEY WORDS: image dehazing; palletization; size distribution; machine vision.

pellets, in which images of green pellets are captured online by an industrial camera under LED lighting and then processed to measure pellet size using image processing algorithms such as noise filtering, pellet segmentation and circle fitting. In order to improve the measuring accuracy of pellet size, many useful algorithms were proposed to cope with uneven illumination and light reflection problems in processing of pellet images.1,4) However, up to now, little effort has been made to the problem of haze removal from green pellet images in the metallurgical process, as pre-sented in Fig. 1(c) for example. The haze mainly results from dust or steam cloud produced by wet raw materials and spray water during pelletization process. The image quality deteriorates significantly in the presence of haze, resulting in errors in image segmentation and measurement of pel-let size. To solve this problem, it is necessary to employ a dehazing algorithm at the beginning of image processing of pellet images.

Currently, various image dehazing methods have been

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proposed for processing of real images captured in outdoor natural hazy environment or synthetic images with haze generated artificially using computer. As shown in Table 1 for example, most of these dehazing methods can be classified into three groups: (1) multiple images dehazing methods,5,6) (2) single image dehazing methods7–14) and (3) methods based on convolutional neural network (CNN).15–17) The first group of dehazing methods require multiple refer-ence images to restore the scene depth information and are thus limited in online machine vision tasks.7) In contrast, single image dehazing methods employ typical features of haze-free images, which is better for real-time applications and has gained significant progress in the past few years.18) At present, most single image dehazing methods adopt the dark channel prior (DCP) proposed by He et al.,9) which is based on properties of so called “dark pixels” with at least one color channel has a pixel value near to zero, besides the sky region. A review of DCP-based image dehazing algo-rithms was performed by Lee et al.,19) in which their differ-ences in the four major steps (atmospheric light estimation, transmission map estimation, transmission map refinement and image restoration) were analyzed in detail and will be therefore not repeated here. Deep learning has exploded over the past few years, so CNN is becoming a promising technique for transmission map estimation.15–17) Neverthe-less, the training and computing of CNN models consume much time and is therefore limited in practical applications, especially in online usages.

In summary, we are faced with abundant choice of image dehazing algorithms. However, current algorithms are mostly focused on processing of real images captured in outdoor natural hazy environment or synthetic images with haze gen-

Fig. 1. Pelletization process in steel industry. (a) schematic dia-gram of pelletization process using rotary disc equipment to produce iron ore green pellets, (b) dust/steam cloud occurring in pelletization process and (c) hazy pellet images to be processed. (Online version in color.)

Table 1. An overview of the existing dehazing algorithms. (Online version in color.)

Dehazing methods Authors Hazy images to be processed

(some examples)

Multiple images dehazing methods

Schechner et al.5)

Narasimhan & Nayar.6)

Single image dehazing methods

Kim et al.7)

Wang et al.8)

He et al.9)

Wang et al.10)

Meng et al.11)

Shiau et al.12)

Berman et al.13)

Zhu et al.14) (synthetic image)

CNN-based dehazing methods

Cai et al.15) (synthetic image)

Li et al.16)

He Zhang & Patel.17)

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erated by computer. Little effort has been made to solve the image dehazing problem in online monitoring of industrial processes, such as the pelletization process in steel industry. The questions are: (1) can we directly adopt current image dehazing algorithms for pellet images, which are covered with dust/steam clouds and captured indoor under LED light instead of atmosphere light? (2) which dehazing algorithm has better performance in both accuracy and computing efficiency to meet the requirement for online monitoring of pelletization process? In order to answer the above questions, we will perform experimental analysis of several state-of-the-art image dehazing methods, using our dataset which contains hazy pellet images captured from pelletization process in a local steel company. Our main work includes:

(1) Establishment of a new dataset, which contains 30 pel-let images covered by slight, moderate or dense dust/steam clouds. Different to synthetic haze images in some datasets reported in literatures,20–22) the haze images in our dataset were captured from indoor pelletization process in dusty and moist environment under LED lighting. This dataset could be used as a benchmark to evaluate the dehazing perfor-mance of different dehazing algorithms for machine vision tasks in online process monitoring.

(2) Experimental testing of five state-of-the-art image dehazing methods on our dataset.

(3) Qualitative and quantitative performance evaluation of the five state-of-the-art image dehazing methods, using conventional criteria as well as task-driven criteria.

(4) Analysis of possible improvements of image dehazing algorithms for online process monitoring in steel company and future work.

The remaining part of the paper is organized as follows: In Section 2, the atmospheric scattering model and the five state-of-the-art image dehazing algorithms to be evaluated are studied. In Section 3, our dataset containing hazy pellet images are described. Section 4 gives experimental analy-sis and performance evaluation of the five image dehazing algorithms. In Section 5, we discuss the limitation of these dehazing methods when used for haze removal of pellet images, and point out possible improvements in future work.

2. Model Study

2.1. The Atmospheric Scattering Model in Hazy Condi-tion

As presented in Fig. 2, when the reflected light of objects pass through the haze medium, the turbid medium will scatter the light to all directions, which degrades medium transmission and leads to poor visibility and contrast of the acquired images.23) According to this theory, the light reaching to the camera sensor mainly consists of two main components: direct attenuation (the red dotted line in Fig. 2) and airlight (the green dotted line in Fig. 2).

This atmospheric scattering model can be expressed mathematically as:

I x J x t x A t x( ) ( ) ( ) ( ( ))� � �1 .................... (1)

where x represents the pixel coordinates in image, I(x) rep-resents the captured hazy image, J(x) is the haze-free image, A denotes the atmospheric light and t(x) is the transmission map. With the atmospheric scattering model, the restored haze-free image can be expressed by:

J xI x A

t xA( )

( )

( )�

�� .......................... (2)

The solving process of Eq. (2) is an ill-posed problem and has two unknown parameters, i.e. t(x) and A. At present, the most advanced image dehazing methods generally utilize Eq. (2), but the main difference lies in the determination of transmission matrix and atmospheric light.

2.2. Image Dehazing Algorithms to be EvaluatedFor the purpose of haze removal from pellet images,

five typical dehazing algorithms are selected for analysis in the present work, including Dark-Channel Prior (DCP) proposed by He et al.,9) Optimized Contrast Enhancement (OCE) proposed by Kim et al.,7) Boundary Constrained Context Regularization (BCCR) proposed by Meng et al.,11) Non-local Image Dehazing (NLD) proposed by Berman et al.13) and All-in-One Dehazing Network (AOD-Net) pro-posed by Li et al.16) The five dehazing algorithms are chosen

Fig. 2. The atmospheric scattering model in hazy condition.24) (Online version in color.)



© 2021 ISIJ 4

for the reason that these methods cover various dehazing method theories, which are very representative. Their dehaz-ing performance has also been validated on public datasets. Moreover, the authors of these dehazing algorithms have made their source code public, so that we can achieve its dehazing performance more accurately when used for hazy pelletization process images. The known parameters (input), estimation results (output), adjustable parameters and source code download address of these methods are shown in Table 2.

In the following texts, we will describe briefly the theory and parameter adjustment of the five image dehazing algo-rithms in Table 2.

2.2.1. Dark-Channel Prior (DCP)The DCP method9,25) assumes that at least one channel of

RGB has a pixel value is near to zero in an outdoor haze-free image. The dark channel is defined as follows:

J x J ydark

y x c r g b

c

r

( ) min ( min ( ))( ) { , , }

��

.................. (3)

where Ω r(x) represents a small patch located in the center of x with size of R × R, and Jdark(x)→0. The key parameter in this method is the size of the small patch. According to DCP theory, it is better to choose larger patch size. However, too larger size may cause halos effect in the restored image.9) To determine the patch size, the author in DCP method9) has conducted a trial-and-error process and found that a path size of 15 × 15 is more appropriate. In our experiment, we set the patch size of 15 × 15 according to the advice in DCP method.9) The other parameters including radius r and regularization ε of guided filter are set to 20 and 10 −3 just as in reference 25).25)

2.2.2. Optimized Contrast Enhancement (OCE)The OCE method7) restores the haze-free image based on

the observation that the image contrast of haze-free image is much higher than haze image. Accordingly, the optimal transmission map t(x) can be estimated by maximizing the image contrast of each small patch. The contrast loss func-tion of patch Ω can be defined as:

E J p J

N

I p I

t Ncontrast

c c

pc r g b

c c

pc

� ��

� ��

�� ( ( ) ) ( ( ) )

{ , , } �� 2

��{ , , }r g b

.......................................... (4)

where NΩ is the number of pixels in block Ω, and I c is the average value of I c(p) in block Ω. In order to avoid the underflow and overflow phenomena in some pixel value, an information loss cost is also defined:

E J p J plossc c

p Bc r g b

� � �� {(min{ , ( )}) (max{ , ( ) }) }

{ , , }

0 0 2552 2

.......................................... (5)

The optimal transmission value of t in a block can be then estimated by minimizing the sum of Econtrast and Eloss:

min( )t

contrast L lossE E� � ........................ (6)

where λL denotes a weight value which controls the relative importance between Econtrast and Eloss. The patch size in OCE method is 32 × 32. We conduct a trial-and-error process and find that only too small patch size (such as 5 × 5) may cause low image contrast in the restored image. Furthermore, the patch size 30 × 30 achieves a better visual perception for the restored image. Another parameter λ has influence on the number of truncated pixel value (or information loss). Figure 3 gives an example of dehazing results with different values of λ (λ = 1, 5, 8). It can be seen that small values

Table 2. Parameters of the selected dehazing algorithms.

Methods Known parameters

Estimation results Adjustable parameters Source code

DCP9) Single hazy image I(x) t(x) and A Patch size, radius and regularization

of guided filter. http://kaiminghe.com/eccv10/index.html

OCE9) Single hazy image I(x) t(x) and A Patch size, radius and regularization

of guided filter. http://mcl.korea.ac.kr/projects/dehazing/#userconsent#

BCCR11) Single hazy image I(x) t(x) and A Bounded parameters, window size

and regularization parameter. http://www.escience.cn/people/menggaofeng/Publication.html

NLD13) Single hazy image I(x) t(x) and A Radiometric correction parameter. http://www.eng.tau.ac.il/~berman/NonLocalDehazing/

AOD-Net16) Single hazy image I(x) J(x) Deep learning training parameter. https://sites.google.com/site/boyilics/website-builder/project-page

Fig. 3. Parameter study for OCE method.



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of λ may result in unnaturally dark pixels to the restored image (see Figs. 3(b) and 3(c)). The λ = 8 achieves a bal-ance between haze removal and information loss for the restored image (see Fig. 3(d)). The value of radius (r = 41) and regularization (ε = 10 −3) in guided filtering process are set as recommended in the OCE method.7)

2.2.3. Boundary Constrained Context Regularization (BCCR)

The BCCR method11) extends the theory of dark channel prior in estimating the initial values of transmission map based on the inherent boundary constraint. Since the radi-ance intensity of a haze-free image Jc in a small patch Ω is always constrained in range of [C0 C1], the initial transmis-sion map is calculated as:

t x

A I

A C

A I

A Cb

c r g b

c c

c c

c c

c c( ) min max , ,

{ , , }�

��

��

��

��

��

��

�� 0 1

1��

��........ (7)

where Cc0 and Cc

1 represents the RGB channel of C0 and C1, respectively. Just as done in other methods,7,9) the transmis-sion value is considered to be a constant in a small patch. Then the final transmission map can be estimated as:

t t zy z

bx y

�� minmax ( )

� � ............................ (8)

There are four adjustment parameters in the BCCR method, including patch size, bounded value, weighting function parameter and regularization parameter. In our experiment, it was found that the key parameter affecting the dehazing result is the patch size, as shown in Figs. 4(b)–4(d). It can be seen that the small patch size can improve the image contrast of the restored image, but it brings over-enhancement to the background (see Fig. 4(b)). The restored image seems more natural at patch size 25 × 25 (see Fig. 4(d)). Therefore, we choose 25 × 25 as patch size for the BCCR method. By conducting a trial-and-error process, we find that the other parameters including bounded value and regularization parameters can be set just as in BCCR methdod.11) The bounded value [C0 C1] is set to [20 300]. The regularization parameter λ and weighting function parameter α is set to 1.0 and 0.5, respectively.

2.2.4. Non-local Image Dehazing (NLD)Different from the current dehazing methods that divide

the input haze image into small patches to estimate the transmission map, the NLD method13) assumes that the colors of a clean image consist of a few hundred individual colors called haze-line. The hazy image can be expressed as:

I x r x x xA( ) [ ( ), ( ), ]� � �( ) ...................... (9)

where IA(x) = I(x) − A, I(x) − A = t(x)[J(x) − A]. Then the transmission map t (x) can be estimated as:

t x r x r( ) ( ) / max= ........................... (10)

where r J x Amax ( )� � .We test the NLD method on the proposed image dataset

which contains images with different haze levels. In the process of our experimental testing, the parameters are set according to the recommendations of the authors in NLD method.13) This is mainly due to the reason that these param-eters have been tested experimentally in NLD method,13) and also can achieve a good visual perception effect on our dataset. There are four parameters in NLD method including sampling frequency, number of haze-lines and two regular-ized parameter. The specific parameters are set as follows: the sampling frequency of a unit sphere N = 1 000, the number of haze-lines K = 40, weight parameter λ = 0.1 and standard deviation value σ (x) = 2.

2.2.5. All-in-One Dehazing Network (AOD-Net)AOD-Net16) is a kind of convolutional neural network

(CNN). Unlike other CNN-based dehazing methods that estimate the transmission map and airlight separately, the AOD-Net generates haze-free image directly from a light-weight CNN. The advantage of this method is that it can estimate the transmission image and air light at the same time and alleviate the cumulative error. In AOD-Net method, Eq. (1) is re-expressed as:

J x K x I x K x b( ) ( ) ( ) ( )� � � .................. (11)

where b is a bias value set to 1, and K(x) is:

K x

t xI x A A b

I x( )

( )( ( ) ) ( )

( ).�

� � �

�

1

1

.............. (12)

Since K(x) changes with the input haze image I(x), a K-estimation module based on deep learning model was designed.

The main parameters need to be adjusted in AOD-Net model including training epochs, learning rate and batch size. In order to ensure the convergence of the model in the training process, we set the parameters in experimental test-ing according to the author’s advice in AOD-Net.16) So the model is trained for 40 epochs. The batch size and learning rate is set to 8 and 0.001, respectively.

The specific parameter settings for these five dehazing methods are shown in Table 3.

Fig. 4. Parameter study for BCCR method.



© 2021 ISIJ 6

3. Image Dataset for Model Testing

In order to examine the aforementioned image dehazing algorithms in processing pellet images, it is necessary to employ suitable testing datasets. Some researchers have provided useful datasets for testing different dehazing algo-rithms, as listed in Table 4 for example.

Fattal et al.21) provided a dataset containing 12 synthetic images which created by using clean images with known depth information. Tarel et al.20) provided the dataset FRIDA containing 420 synthetic hazy images. The D-HAZY data-set provided by Ancuti et al.22) contains 1 400 synthetic indoor hazy images. Li et al.18) introduced a large scale benchmark dataset named RESIDE which synthetic and real hazy images. However, these datasets only focus on image scenes such as hazy cityscapes, road or rooms. To our best knowledge, no effort has been made to establish benchmark dataset for hazy images from industrial manufacturing (such as pelletization process). Moreover, most hazy images in these datasets are synthetic images generated from haze-free images based on Eq. (1). Although it is convenient to use these synthetic images to test the performance of dehazing algorithms, we cannot ignore the fact that there is difference between synthetic and real hazy images.18) Therefore, we

established our own dataset PELLET to test the aforemen-tioned five state-of-the-art dehazing algorithms. It contains 30 hazy pellet images (656 × 490), which were captured by machine vision system installed in front of the rotary disc in a local steel company(as shown in Fig. 5). The rotary disc has a diameter of 6 meters and a rotation speed of 8 rpm. The machine vision system including an industrial camera (Baumer VLG12) and two LED lights were used to moni-tor the pellets falling out of the rotary disc. Under certain process conditions, the rotary disc was surrounded by dust/steam cloud, resulting in hazy pellet images. According to the haze level, we classified those images into three types in our PELLET dataset, i.e. dense hazy image, moderate hazy image and slight hazy image, and each type contains 10 images (see Fig. 6). Details about the machine vision system can be found in our previous publication.4) Compared with the hazy image datasets mentioned above, the hazy images in PELLET dataset have the following characteristics:

(1) The machine vision monitoring systems of manufac-turing processes generally adopt grayscale cameras to sat-isfy real-time demands and the backgrounds of green pellet images are gray and black. Thus, the color information of

Table 4. An overview of different hazy image datasets. (Online version in color.)

Datasets Scenes Image type Example

FRIDA by Tarel et al.20) Hazy road image Synthetic hazy images

Fattal et al.21) Natural scene image Synthetic hazy images

D-HAZY by Ancuti et al.22) Indoor scenes Synthetic haze images

RESICE by Li et al.18) Indoor and outdoor scenes Synthetic and real hazy images.

Ours own dataset PELLET Hazy image captured from industrial pelletization Real hazy images.

Fig. 5. Schematic of the designed machine vision system. (Online version in color.)

Table 3. Parameter settings for these five dehazing methods.

Methods Parameter settings

DCP9) Patch size 15 × 15, radius r = 20, regularization ε = 10 −3

OCE7) Patch size 30 × 30, weight parameter λ = 8, radius r = 41, regularization ε = 10 −3

BCCR11)Patch size 25 × 25, regularization λ = 1.0, weighting function parameter α = 0.5, bounded value [C0 C1] = [20 300]

NLD13)Sampling frequency N = 1 000, number of haze-lines K = 40, weight parameter λ = 0.1, standard deviation value σ (x) = 2

AOD-Net16) Training epochs 40, batch size 8, learning rate 0.001



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hazy pellet images is not available.(2) The images in PELLET dataset are real hazy pellet

images captured during the pelletization process in a steel company. Moreover, The PELLET dataset contains pellet images with different haze levels.

4. Experimental Results and Discussions

With PELLET dataset, it is now possible to perform experiments to test the dehazing performance of the five state-of-the-art dehazing algorithms (see Table 2) in pro-cessing industrial hazy images. In this section, various criteria will be employed for evaluation and comparison of the dehazing performance of different algorithms, including visual perceptual evaluation, quantitative evaluation using different criteria, task-driven comparison and computing time comparison. In the experiment, we tried our best to use the author’s open source code provided by those authors themselves.

4.1. Visual Perceptual EvaluationVisual perceptual evaluation is a method that was pro-

posed by Ma et al.26) to evaluate the image dehazing algo-rithms, where some participants were asked to evaluate the quality of restored image based on visual perception. Each participant then gave a score ranging between 1 and 10, with 1 representing the worst image quality and 10 the best. In our experiments, the five dehazing algorithms (OCE, DCP, BCCR, NLD and AOD-Net in Table 2) were adopted for processing all the 30 hazy pellet images in our dataset, including 10 dense hazy images, 10 moderate hazy images and 10 slight hazy images. Twenty participants were invited to make perceptual evaluation of the dehazing performance of different methods. The average score of each method is shown in Fig. 7, and the dehazed images generated by these methods are given in Fig. 8 for example.

Results in Figs. 7 and 8 demonstrate that the AOD-Net method, which is based on deep learning network and syn-

thetic hazy images for network training, obtains the lowest score (2.78–3.5) in processing hazy pellet images. This could result from the content variance between synthetic and real hazy images. The DCP method, which assumes that the RGB channels of images without haze have one pixel value close to zero in a small patch, have higher scores than AOD-net (around 4.5–4.67). Nevertheless, the pellets in the recovered images are still difficult to distinguish, especially in the case of dense haze images. It may result from dark channel prior, which is based on statistics of natural scene images but might not be suitable for hazy images captured in industrial processes. The OCE method and the BCCR method obtain comparable scores (5.89 vs. 5.78, 6.61 vs. 6.22) when processing pellet images with slight and moder-ate haze. But BCCR performs much better than OCE when dealing with pellet images with dense haze (7.33 vs. 4.5). Compared to other four methods, the NLD method achieves the highest score in processing hazy pellet images with slight, moderate or dense haze (scored 7.5, 7.28, and 7.44 respectively). The clarity of the pellet objects and image contrast of the restored pellet images are greatly improved. Nevertheless, NLD method seems to bring over enhance-

Fig. 6. Different levels of hazy images in our dataset.

Fig. 7. Comparison of the average score of different dehazing methods in perceptual evaluation. (Online version in color.)



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ment to the image background and slight tonal variation to the whole image.

4.2. Quantitative Evaluation using Non-reference CriteriaExcept for qualitative perceptual evaluation described

above, it is also necessary to evaluate the image dehazing methods quantitatively. PSNR (Peak Signal to Noise Ratio) and SSIM (Structural Similarity Index) are two criteria that are commonly used. To calculate PSNR and SSIM values, pairs of haze and haze-free ground truth images must be provided. This requirement cannot be met because it is impossible to simultaneously capture haze and haze-free images in the pelletization process. For this reason, we will adopt two no-reference evaluation criteria in the pres-ent work for quantitative evaluation of the five dehazing methods, i.e. SSEQ (Spatial–Spectral Entropy-based Quality index)27) and FADE (Fog Aware Density Evaluator).28) The SSEQ index extracts the local spatial and spectral entropy features in distorted images, then a support vector machine model is adopted to train these image distortions (noise, blur and fastfading) and predict the image quality. The FADE makes full use of measurable deviations in haze and haze-free images based on statistical regularities observation. The observed features of hazy image include faint color, low contrast and shifted luminance. After the extraction of these features, a multivariate Gaussian model is adopted to estimate the haze density of a test hazy image. The results are shown in Table 5. In this table, the value of SSEQ and FADE ranges from 0 to 100, with 0 representing the worst image quality and 100 the best quality.

Table 5 clearly shows that the BCCR and NLD methods get higher scores (and thus better haze removal perfor-mance) than DCP and AOD-net methods in processing pellet images with different haze levels, whereas the perfor-mance of OCE method lies in-between. This is consistent with the result of visual perceptual evaluation. In contrast to the visual perceptual evaluation result, we cannot tell from Table 5 that the NLD method is the best dehazing method of all the five methods, since the BCCR method obtains comparable or even better scores than NLD method in pro-cessing images with slight and moderate haze. Nevertheless, the NLD method shows the best performance in treating with images with dense haze, with the highest score in both

Fig. 8. Comparison of image dehazing results of the five dehazing algorithms. (Online version in color.)

Table 5. Values of SSEQ and FADE by different dehazing meth-ods.

Criteria Haze levelimage dehazing methods

DCP AOD-Net OCE BCCR NLD

SSEQ

slight 80.1 81.0 86.1 90.8 89.6

moderate 71.2 73.8 80.8 81.3 81.3

dense 64.4 67.6 76.5 71.7 78.4

Averaged: 71.9 74.1 81.1 81.3 83.1

FADE

slight 81.6 87.7 87.6 91.1 89.5

moderate 74.2 68.3 74.2 79.9 75.4

dense 64.6 69.3 64.6 71.1 73.5

Averaged: 73.5 75.1 75.5 80.7 79.5



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SSEQ and FADE index.We observed that evaluation results based on non-reference

criteria (SSEQ or FADE) are sometimes contrary to percep-tual evaluation results. For instance, the OCE dehazing method gets a higher SSEQ score than the BCCR method (76.5 vs. 71) in processing dense haze images, however, based on perform perceptual evaluation in Fig. 8, the qual-ity of the recovered image generated by OCE is obviously worse than that of BCCR method.

To make a comprehensive and objective evaluation, it is necessary for us to introduce more criteria to compare these five image dehazing methods, such as task driven compari-son and computing time comparison.

4.3. Task-driven ComparisonDehazing is a pre-processing for many machine vision

tasks. In return, the results of these machine vision tasks can be used as an indirect indicator to assess the dehazing performance of different dehazing methods. Such a process can be called task-driven comparison. Several previous studies have utilized it in evaluation of dehazing perfor-mance.18) The machine vision task for pelletization process is to measure pellet size. To do so, the raw image should be processed first using haze removal algorithm, followed by the procedure of pellet segmentation and circle fitting. The quality of the restored image after haze removal will no doubt greatly affect accuracy of pellet segmentation and consequently the size measurement. Therefore, it’s reasonable for us to indirectly evaluate the five dehazing algorithms by comparing their corresponding image seg-

mentation accuracy.The flowchart of pellet segmentation process is shown in

Fig. 9. Figure 9(c) gives the initial segmentation result of the restored image. After a hole filling operator, the final seg-mented result can be seen in Fig. 9(d). The pellet segmenta-tion can separate the pellets from the background image and eliminate the interference of uneven illumination. Detailed procedures of pellet segmentation can be found in our previ-ous publication4) and will therefore not repeated here.

For assessment of image segmentation accuracy, two commonly used indices, i.e. Intersection of Union(IoU)29) and False Positive Rate(FPR) are applied:

IoUTP

TP FN FP�

� � ....................... (13)

FPRFP

TP FN FP�

� � ....................... (14)

where TP (True Positive TP) is the number of correctly segmented pixels, FN (False Negative) is the number of missed segmented pixels, FP (False Positive) is the number of falsely segmented pixels. A lower value of FPR and a higher value of IoU mean better pellet segmentation results.

In the present work, 30 hazy images from our dataset PELLET were used for task-driven comparison. Figure 10 shows for example a hazy image to be processed, the restored image based on different dehazing methods, and the corresponding image after pellet segmentation. The average values of IoU and FPR corresponding to each dehazing method are listed in Table 6.

Fig. 9. (a) Raw image, (b) restored image, (c) initial segmentation result (d) final segmentation result.

Fig. 10. Results of pellet segmentation after usage of different dehazing images.



© 2021 ISIJ 10

From Fig. 10 we can see that, most pellets can be well segmented based on the restored image by NLD and BCCR dehazing methods. That is why their True Positive (IOU) index is much higher (IOU > 0.72) than the other three methods. However, the False Positive Rate (FPR) of NLD and BCCR method is relatively higher (FPR > 0.17), which means there exists over-segmentation. Over-segmentation is mainly caused by enhancement of image background during haze removal. The influence of over-segmentation on pellet size measurement can be reduced by deleting small clusters of pixels in the subsequent image processing steps.4) Although DCP has the lowest False Positive Rate (FPR = 0.002), it results in under segmentation (see Fig. 10), which will bring difficulties later in pellet extraction and even failure in pellet size measurement. In this aspect, the NLD and BCCR dehaz-ing methods are more advantageous than DCP method.

4.4. Computing Time ComparisonAccording to the evaluations above, the NLD and BCCR

method seem more advantageous than the other three dehaz-ing methods (DCP, OCE, AOD-net) in processing images with different levels of haze. It is now interesting for us to compare the computing time required by these methods.

In the present work, the four image dehazing algorithms (DCP, OCE, BCCR, NLD) were implemented with Matlab programming language on a computer with Intel core i5 3.2 GHz and 12 GB of RAM. The deep learning AOD-Net method runs under Caffe environment. Each dehazing method was used to process 30 hazy images in our dataset PELLET.

The average computing time for one image is presented in Fig. 11. Due to the complexity of its dehazing pro-cess (and thus high quality of restored image), the NLD method costs much more computing time (5 seconds per image) than other four methods, followed by BCCR and

DCP methods(about 2.5 seconds) and OCE (1.7 seconds). Although the deep learning method AOD-net needs the shortest computing time (0.8 seconds), it costs much time in the stage of network training and its haze removal ability is limited. This means that one has to make a compromise between dehazing performance and computing time. From this viewpoint, the BCCR dehazing method could be a good choice for machine vision task in industrial pelletization process where real-time is required.

5. Conclusions and Future Work

5.1. ConclusionsCurrent image dehazing algorithms concentrate mainly

on outdoor hazy images due to natural haze phenomenon in the sky. But how good are these algorithms in solving haze removal problem for images captured from indus-trial processes with dust and steam cloud? To answer this question, we captured hazy pellet images from industrial pelletization process in a steel company and established the hazy image dataset PELLET. Experiments were then performed on the PELLET dataset to test the performance of five state-of-the-art image dehazing algorithms (NLD,13) BCCR,11) OCE,7) DCP,25) AOD-Net,16) using qualitative and quantitative evaluation criteria. It can be concluded from our experiments that:

(1) In visual perceptual evaluation, NLD and BCCR methods show better dehazing performance than the other three methods in processing pellet images with different levels of haze.

(2) In quantitative evaluation using non-reference crite-ria, the NLD and BCCR dehazing methods get higher scores (and thus better haze removal performance) in most cases than other methods, and NLD method seems more advan-tageous in processing pellet images with dense haze. This conclusion is consistent with the result of visual perceptual evaluation. However, we also found that evaluation results based on SSEQ and FADE criteria are sometimes inconsis-tent with results of visual perceptual evaluation.

(3) In task-driven comparison, it was found that more pellets can be segmented based on the restored image by NLD and BCCR dehazing methods than by other methods. However, NLD and BCCR result in over-segmentation problem due to background enhancement in dehazing algo-rithms. The influence of over-segmentation on pellet size measurement can be reduced by deleting small clusters of pixels in the subsequent image processing steps.

(4) In computing time comparison, the NLD method costs the most computing time (5 seconds per image), fol-lowed by BCCR and DCP methods (about 2.5 seconds) and OCE (1.7 seconds). Although the deep learning method AOD-net needs the shortest computing time (0.8 seconds), it costs much time in the stage of network training and its haze removal ability is limited.

Based on our experiments and comprehensive analysis, we would recommend NLD and BCCR dehazing methods because they behave principally better than other three dehazing methods in dealing with pellet images with dif-ferent levels of haze. For machine vision task of pellet size measurement where real-time is required, one has to make a compromise between quality of the restored image

Fig. 11. Average computing time needed by each method. (Online version in color.)

Table 6. Values of IoU and FPR indices of different image dehaz-ing methods.

IndexDifferent dehazing methods

DCP AOD-Net OCE BCCR NLD

IoU 0.627 0.491 0.460 0.727 0.721

FPR 0.002 0.326 0.125 0.173 0.180



© 2021 ISIJ11

and computing time. In this case, we would recommend BCCR method since its computing time is only half of NLD method.

5.2. Future WorkAlthough NLD and BCCR methods show good per-

formance in solving haze removal problem for images captured from industrial processes with dust and steam cloud. However, their ability in processing images with dense haze needs to be further improved, because the SSEQ (Spatial–Spectral Entropy-based Quality index) and FADE (Fog Aware Density Evaluator) indices of the restored image are still less than 80 (full score 100).

In addition, image dehazing is only one of the many pre-processing procedures in online pellet size measure-ment. However, according to our experiments, the dehaz-ing procedure alone costs much time (NLD costs about 5 seconds while BCCR about 2.5 seconds) in haze removal of an image of medium size 656 × 490. The computing time should be reduced further to meet with real-time require-ment in pelletization process monitoring.

AcknowledgementsThis work is supported by National Natural Science

Foundation of China (No. 61973108) and Hunan Key Laboratory of Intelligent Robot Technology in Electronic Manufacturing (IRT.2018001). We also thank Kaiming He, Jin-Hwan Kim, Dana Berman, GaoFeng Meng and Boyi Li for providing source code of their dehazing algorithms in Internet.

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