Digital image processing image restoration and blind deconvolution

Nguyễn Công Phương DIGITAL IMAGE PROCESSING Image Restoration and Blind Deconvolution Contents I. Introduction to Image Processing & Matlab II. Image Acquisition, Types, & File I/O III. Image Arithmetic IV. Affine & Logical Operations, Distortions, & Noise in Images V. Image Transform VI. Spatial & Frequency Domain Filter Design VII. Image Restoration & Blind Deconvolution VIII. Image Compression IX. Edge Detection X. Binary Image Processing XI. Image Encryption & Watermarking XII. Image Classification & Segmentation XIII. Image – Based Object Tracking XIV. Face Recognition XV. Soft Computing in Image Processing sites.google.com/site/ncpdhbkhn 2 Image Restoration and Blind Deconvolution 1. Introduction 2. Image Representation 3. Deconvolution sites.google.com/site/ncpdhbkhn 3 Introduction • Image enhancement (improving image quality for visual purposes) includes: – Noise removal, – Constrast stretching, – Histogram equalization, – Filtering, – Restoration through deconvolution. sites.google.com/site/ncpdhbkhn 4 sites.google.com/site/ncpdhbkhn 5 Image Representation (1) Defection function H(m, n, θ) Σ ( , )f m n ( , ) ( , )g m n f H m n   f H ( )k sites.google.com/site/ncpdhbkhn 6 Image Representation (2) Motion – blurred Out-of-focus blurred Image Restoration and Blind Deconvolution 1. Introduction 2. Image Representation 3. Deconvolution a. Lucy – Richardson Method b. Wiener Method c. Blind Method sites.google.com/site/ncpdhbkhn 7 Deconvolution • The reverse of convolution. • A.k.a. “inverse filtering”. • 3 main types of deconvolution algorithms: – Lucy – Richardson method, – Wiener method, – Blind method. sites.google.com/site/ncpdhbkhn 8 Fourier transformg h f G HF    1 InverseFourier transformF D G f  sites.google.com/site/ncpdhbkhn 9 Lucy – Richardson Method 1t t t gf f h h f       sites.google.com/site/ncpdhbkhn 10 Wiener Method * 2 H FW H F N   sites.google.com/site/ncpdhbkhn 11 Blind Deconvolution