MINI PROJECT ON A NEW REFERENCE-FREE IMAGE QUALITY INDEX FOR BLUR ESTIMATION IN THE FREQUENCY DOMAIN

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

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BY

M.V.S.RAMA RAJU(09591A0461) P.SAIDEEP(09591A0476) UNDER THE GUIDANCE OF K.PRABHU

overview Introduction What is blur? Why blur occurs? Difference between noise and blur? Types of images Block diagram for blur estimation Math content Sample code Different types of filtering techniques Why estimation? Applications References Future scope Conclusion

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IntroductionThis project presents a new reference-free

image quality index based on spectral analysis

The proposed method consists of adding blur to the test image and measuring its impact.

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What is blur?Change in the position of pixels in an image

Original image Blurred image

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How blur occurs ?Blur occurs due to handshake while taking a

picture in a cell cameraBlur occurs due to motion of objectsBlur occurs due to atmospheric conditions

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Difference between noise and blur

Image with noise Image with blur

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Types of imagesGray scale

Indexed

Binary

Rgb

Gray scale

R g b

0 or 1

R g b

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Block diagram for blur estimation

+Blurred image

Fourier transform

Radial analysis

Blur index

Blurpsf(x,y)

o(x,y) +Blurred image +

noise

noisen(x,y)

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r(x,y)

Math contThe discrete Fourier transform of 2D

F(u,v) is then transformed into polar coordinates represented by F(ω, θk).

An expression for the total radial energy is then obtained using the following:

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Sample code %%% calculate the FFT for test and filterd images %%% Ft=fft2(I(:,:,k)); Ff=fft2(bimg(:,:,k)); for i=1:length(Ft) for j=1:length(Ft) ER(i,j)=abs(cart2pol(imag(Ft(i,j)),real(Ft(i,j)))); ERf(i,j)=abs(cart2pol(imag(Ff(i,j)),real(Ff(i,j)))); end end %%%% calculate the BI %%%% wmax=max(max(ER)); BI(:,:,k)=log((1/wmax)*sum(sum(abs(ER-ERf)))); end figure,imshow(dimg);title('Degraded image'); figure,imshow(bimg);title('Blurred image'); bb=(BI(:,:,1)+BI(:,:,2)+BI(:,:,3))/3; disp(['The Blur Index of the image is ',num2str(bb)])

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Different types of filtering techniquesInverse filteringWiener filteringRegularised filtering

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WHY ESTIMATION?

Blurred image Estimation of original image 12

Applications

Cameras at traffic signalsSatellite imagesComputer animation photography

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Future scope:

•Also in future scope, this algorithm can be very useful for the real time video image processing by improving the live feature extraction parameters. •The proposed technique can enhance the future of the multimedia digital image processing by means of super resolution.

conclusion• An efficient no-reference blur quality index is

proposed. The method is not based on edge detection as most existing methods.

• Instead, we use a basic radial analysis in the frequency domain to measure the impact of blur added to the original image.

• The obtained results in terms of correlation with the subjective tests prove the efficiency of the proposed method.

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References:

Freeman, W.T.; Pasztor, E.C.. Learning low-level vision, CVPR 1999, 182-1189 vol.2

William T. Freeman, Thouis R. Jones, and Egon C. Pasztor, Example-based super-resolution, IEEE Computer Graphics and Applications, March/April, 2002

Baker, S.; Kanade, T. Hallucinating faces. Automatic Face Gesture Recognition, 2000, 83-88.

Ce Liu; Heung-Yeung Shum; Chang-Shui Zhang. A two-step approach to hallucinating faces: global parametric model and local nonparametric model. CVPR 2001. I-192-8.

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Queries

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