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EEE 502 Pattern Recognition

Project TitleDe-Blurring of Digital Image

Prepared by : SYED ATIF NASEEM

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Overview

Topics covered:

General Method of Degradation

Bayesian Approach.

MAP Approach

Blind Deconvolution

Lucy Richardson Damper

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IntroductionUse two different approaches to reduce the blur in the digital image based

on our prior knowledge of Blurring and noising present in the image.

Blind Deconvolution :

limited input data A single distorted image is available. Implements blind deconvolution algorithm with out the knowledge of PSF

Lucy Richardson Deconvolution :

Limited Input data. Implements Accelerated Damper alogorithm. Varying its Parameter e:g Damper to get the image in acceptable condition

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General Method Of Image Degradtion

Blur Can be defined as

Degradation Operator

g = DLHu + n

Operator L denotes lens distortions Blurring operator H describes the external and internal radiometric degradationsD is an operator modeling the camera sensorn stands for additive noise and U is the original image.

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Bayesian view of Solution

If Degradation operator is known, Image can be deblurred by minimum of the functional.

First term Describes as a error.Q(u) is a regularization term that corresponds to the negative logarithm of the prior probability of the image u. Noise variance is also present .

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Approximate Map Approach

Estimate the PSF in advance and then proceed with non-blind restoration by minimization over the possible images u.

MAP approach may not give optimal result especially if we do not have enough information.

Multiple image, MAP approach is appropriate.

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Blind Deconvolution

Blind deconvolution algorithm can be used effectively when no information about the distortion.

Deconvoblind function restores the image and the PSF simultaneously by using an iterative process similar to the accelerated, damped Lucy-Richardson algorithm.

Make an initial guess at the size of the PSF or specify an array of 1’s as the initial PSF.

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Results of Blind Deconvolution Algorithm

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Lucy RichardSon Deconvolution

It can be effective when we know the PSF but know little about the additive noise in the image.

Reduce the effect of noise amplification on image restoration.

Account for nonuniform image quality i.e bad pixels, flat-field variation.

Improve the restored image resolution by subsampling.

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Results of Lucy Richardson Deconvolution

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Thank You