Investigation of an Image Restoration Method: Blind Image Deconvolution

• Main Authors: Ming-Hsiu Chien, 簡名秀
• Other Authors: Pei-Chen Lo
• Format: Others
• Language: zh-TW
• Published: 2000
• Online Access: http://ndltd.ncl.edu.tw/handle/11495589008462503255

碩士 === 國立交通大學 === 電機與控制工程系 === 88 === Classical linear image restoration techniques assume that the linear shift invariant blur, also known as the point-spread function (PSF), is partially known prior to restoration. In many practical situations, however, the PSF is unknown and the problem of image restoration involves simultaneously identifying both the true image and PSF from the degraded observation. Such a process is referred to as the blind deconvolution. This thesis introduces a novel blind deconvolution technique for the restoration of linearly degraded images without explicit knowledge of either the original image or the point spread function. The method is called the non-negativity and support constraints recursive inverse filtering (NAS-RIF) algorithm. The technique applies to situations in which the scene consists of a finite support object against a uniform background. The information required includes the non-negativity of the true image and the supporting region of the original object. The procedure involves recursive filtering of the blurred image to minimize a convex cost function. We focus on the study of two factors in implementing the technique: one is the blurring process or characteristics of the point-spread function, and the other is the initialization of the filter parameters. When the size of the inverse of the degraded function is large, the performance of the introduced algorithm by the small sized FIR filter will be limited. Besides, with proper initial condition, the recursive cycles will be reduced. English Abstract Acknowledgement Contents List of Figures List of Tables Chapter 1 INTRODUCTION 1.1 Background 1.2 Motivation 1.3 Outline of this Thesis Chapter 2 BLINE IMAGE DECONVOLUTION 2.1 Preface 2.2 Brief Introduction of Existing Approaches 2.3 Problem Formulation Chapter 3 THE NONNEGATIVITY AND SUPPORT CONSTRAINTS RECURSIVE INVERSE FILTERING (NAS-RIF) 3.1 Nonparametric Deterministic Image Restoration 3.2 The NAS-RIF Algorithm Chapter 4 EXPERIMENT AND RESULTS 4.1 Characteristics of Typical Images and Point-Spread Functions for Simulations 4.2 Image Restoration Results 4.3 Analysis and Discussion Chapter 5 CONCLUSIONS Reference Appendix A Appendix B