Image restoration phd thesis

Image restoration phd thesis

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Image restoration phd thesis

Dash, Ratnakar Parameters Estimation For Image Restoration. PhD thesis. Image degradation generally occurs due to transmission channel error, camera mis-focus, atmospheric turbulence, relative object-camera motion, etc. Such degradations are unavoidable while a scene is captured through a camera. As degraded images are having less scientific values, restoration of such images is extremely essential in many practical applications.

In this thesis, attempts have been made to recover images from their degraded image restoration phd thesis. Various degradations including, out-of-focus blur, motion blur, atmospheric turbulence blur along with Gaussian noise are considered. Basically image restoration schemes are based on classical, regularisation parameter estimation and PSF estimation.

In this thesis, image restoration phd thesis, five different contributions have been made based on various aspects of restoration. Four of them deal with spatial invariant degradation and in one of the approach we attempt for removal of spatial variant degradation.

Two different schemes are proposed to estimate the motion blur parameters. Two image restoration phd thesis Gabor filter has been used to calculate the direction of the blur. Radial basis function neural network RBFNN has been utilised to find the length of the blur. Subsequently, Wiener filter has been used to restore the images. Noise robustness of the proposed scheme is tested with different noise strengths. The blur parameter estimation problem is modelled as a pattern classification problem and is solved using support vector machine SVM.

Image restoration phd thesis length parameter of motion blur and sigma σ parameter of Gaussian blur are identified through multi-class SVM. Support vector regression SVR has been utilised to obtain a true mapping of the images from the observed noisy blurred image. The parameters in SVR play a key role in SVR performance and these are optimised through particle swarm optimisation PSO technique.

The optimised SVR model is used to restore the noisy blurred images, image restoration phd thesis. Blur in the presence of noise makes the restoration problem ill-conditioned. The regularisation parameter required for restoration of noisy blurred image is discussed and for the purpose, a global optimisation scheme namely PSO is utilisedto minimise the cost function of generalised cross validation GCV measure, which is dependent on regularisation parameter.

This eliminates the problem of falling into a local minima. The scheme adapts to degradations due to motion and out-of-focus blur, associated with noise of varying strengths. In another contribution, an attempt has been made to restore images degraded due to rotational motion. Such situation is considered as spatial variant blur and handled by image restoration phd thesis this as a combination of a number of spatial invariant blurs.

The proposed scheme divides the blurred image into a number of images using elliptical path modelling. Each image is deblurred separately using Wiener filter and finally integrated to construct the whole image. Each model is studied separately, and experiments are conducted to evaluate their performances. The visual as well as the peak signal to noise ratio PSNR in dB of restored images are compared with competent recent schemes.

Repository Staff Only: item control page. Parameters Estimation For Image Restoration. Abstract Image degradation generally occurs due to transmission channel error, camera mis-focus, atmospheric turbulence, image restoration phd thesis, relative object-camera motion, etc.

NIT Rourkela. Powered by. Login Create Account. Parameters Estimation For Image Restoration Dash, Ratnakar Parameters Estimation For Image Restoration. PDF 6Mb. Image restoration, out-of-focus blur, motion blur, SVM, SVR, multi-class SVM, blind image deconvolution, regularisation, spatial variant blur,point spread function.

Hemanta Biswal.

Image Restoration Basics and Inverse Filter

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