IMAGE ENHANCEMENT

Introduction Noise model & its types Filtering

Introduction

The principal goal of restoration techniques is to improve an image in some predefined sense.

Image restoration – objective process, attempts to recover image using priori knowledge of degradation phenomenon & applying the inverse process in order to recover the original image.

Image enhancement – Subjective process, consist of a collection of techniques that seek to improve the visual appearance of an image or to convert the image to a form better suited for analysis by a human or a machine. In an image enhancement system, there is no conscious effort to improve the fidelity of a reproduced image with regard to some ideal form of the image, as is done in image restoration.

Introduction

The relevant features for the examination task are enhanced

The irrelevant features for the examination task are removed/reduced

Specific to image enhancement:

- input = digital image (grey scale or color)

- output = digital image (grey scale or color)

Examples of image enhancement operations:

noise removal;

geometric distortion correction;

edge enhancement;

contrast enhancement;

image zooming;

image subtraction;

pseudo-coloring. Classification of image

enhancement operations: Based on the type of the

algorithms: grey scale transformations; spatial operations; transform domain processing; pseudo-coloring

Based on the class of applications – as in the examples above.

Noise

It is defined to be any degradation in the image , caused by external disturbance.

Sources- from a noisy sensor or channel transmission errors.

Pixels that are in error often appear visually to be markedly different from their neighbors.

Noise models

Based on the statistical behavior of the intensity values in the noise component.

1. Gaussian Noise modelII. Rayleigh Noise modelIII.Erlang Noise modelIV.Exponential Noise modelV. Uniform Noise modelVI. Impluse Noise model

Noise Models

Gaussian noise model Rayleigh noise model

Noise model

Erlang Noise Exponential Noise

Noise model

Uniform noise Impulse noise

Types of noise

Photo electronicphoton noisethermal noise

Impulsesalt noisepepper noisesalt and pepper noiseline drop

Structuredperiodic, stationaryperiodic, nonstationaryaperiodicdetector stripingdetector banding

Structured Noise

Periodic, nonstationary

•noise parameters (amplitude, frequency, phase) vary across the image.

•Intermittent interference between electronic components.

Structured noise

Periodic, stationary

Noise has fixed amplitude, frequency and phase .

Commonly caused by interference between electronic components.

Structured noise

Aperiodic

JPEG noise

ADPCM (Adaptive Pulse Code Modulation) noise

Structured noise

Detector Striping

Calibration differences among individual scanning detectors

Filtering

It refers to accepting or rejecting certain frequency components

Different types of filters. We are going to see about the following.

Band reject filters Bandpass filters Notch filters

Periodic noise

Periodic Noise Reduction by Freq. Periodic Noise Reduction by Freq. Domain Filtering Domain Filtering

Periodic noise

can be reduced by

setting frequency

components

corresponding to

noise to zero.

Band Reject FiltersBand Reject Filters

Removing periodic noise form an image Removing periodic noise form an image involves removing a particular range of involves removing a particular range of frequencies from that imagefrequencies from that image

Band rejectBand reject filters can be used for this filters can be used for this purposepurpose

Use to eliminate frequency components in some bands

Band Pass Filter

It performs the opposite operation of a band reject filter.

It generally removes too much image detail.

It is useful in isolating the effects of an image.

It helped to isolate the noise pattern.

Notch filter

A notch filter rejects (or passes) frequencies in predefined neighborhoods about a certain frequency.

The shape of notch areas should be arbitrary.

It appears in symmetric pairs about the origin.