Spectral Based Blur Classification and Parameter Estimation Approaches for image Restoration

Abstract

Extracting information from a blurred image without any prior knowledge of image, its blurring and sensing mechanism is of great interest in image processing and vision based systems. The image restoration process requires blur type classification and extracting parameters of sensing mechanism. One approach is spectral based blur classification in blind image deconvolution. Identifying the blur kernel, also known as point-spread-function (PSF) and then restoring image using non-blind methods has many solutions in various domains spatial, spectral and transform, but they are complex and give limited quality results. Blind deconvolution requires joint estimate of PSF and original image, which becomes iterative or recursive, and incorporate some prior knowledge about process. newlineThis thesis presents Blur identification mechanisms using novel spectral approach that is invariant based, Random Forest classifier based using skewness and kurtosis parameters, and novel application of MNIST CNN architecture. newlineThis thesis also proposes spectral based new framework for estimation of motion and defocus blur parameters. Motion blur parameter estimation is based on innovative formulation of trigonometric relation between spectral lines to relate blur length and angle. The MNIST CNN architecture is applied for motion blur parameter estimation. In this thesis, four methods are proposed for defocus blur parameter estimation. First technique estimates the defocus blur radius from the radius of spectral nulls of the blurred image using the proposed First White method. Second method estimates the defocus radius by determining the signature of radius of the spectral nulls of the blurred image. The third method formulates a polynomial relation between radius of this spectral null and actual radius of defocus blur. The fourth method employs random forest classifier to estimate the blur radius using a proposed set of features. The combination of random forest classifier and proposed feature set adds novelty to defocus parameter estimation