The Highly Lose Image Inpainting Method Based on Human Vision

[[abstract]]Currently, noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. In order to restore damaged image data effectively, we propose a novel image inpainting technique based on wavelet transformation. The primary feature of our proposed technique is to separate the given image into two principal components which encompass image texture and color respectively. Then, according to the distinctive qualities of the given image, various image inpainting methods are adopted to perform image repair. By taking advantage of the separation of an image into its individual frequency components, we use the multi-resolution characteristics of wavelet transform, from the lowest spatial-frequency layer to the higher one, to analyze the image from global-area to local-area progressively. In order to substantiate the effectiveness of our proposed image inpainting method, we employed various images subject to high noise interference and/or extensive data loss or distortion. The experimental results were perfect, even if the distortion portions of the repaired images were higher than 90%[[notice]]補正完畢[[conferencetype]]國際[[conferencedate]]20060901~20060901[[conferencelocation]]Beijing, Chin

Image Inpainting with Improved Storage Capability using DCT

Nowadays, tremendous amount of digital media is generating but having grazes on it. In this paper we proposed new idea to remove the graze from image, called image inpainting where in, Exemplar based image inpainting follows discrete cosine transform . The exemplar based image inpainting is based on copy-and-paste texture synthesis for reconstructing damaged parts of an image. Based on exemplar-matching techniques performance and speed of algorithm increases but size of image is also increases so, we proposed discrete cosine transform for reducing the size of image, removing noise and ultimately provides it good quality of image

Weighted Nuclear Norm Minimization Based Tongue Specular Reflection Removal

In computational tongue diagnosis, specular reflection is generally inevitable in tongue image acquisition, which has adverse impact on the feature extraction and tends to degrade the diagnosis performance. In this paper, we proposed a two-stage (i.e., the detection and inpainting pipeline) approach to address this issue: (i) by considering both highlight reflection and subreflection areas, a superpixel-based segmentation method was adopted for the detection of the specular reflection areas; (ii) by extending the weighted nuclear norm minimization (WNNM) model, a nonlocal inpainting method is proposed for specular reflection removal. Experimental results on synthetic and real images show that the proposed method is accurate in detecting the specular reflection areas and is effective in restoring tongue image with more natural texture information of tongue body

Image inpainting by global structure and texture propagation.

Huang, Ting.Thesis (M.Phil.)--Chinese University of Hong Kong, 2008.Includes bibliographical references (p. 37-41).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Related Area --- p.2Chapter 1.2 --- Previous Work --- p.4Chapter 1.3 --- Proposed Framework --- p.7Chapter 1.4 --- Overview --- p.8Chapter 2 --- Markov Random Fields and Optimization Schemes --- p.9Chapter 2.1 --- MRF Model --- p.10Chapter 2.1.1 --- MAP Understanding --- p.11Chapter 2.2 --- Belief Propagation Optimization Scheme --- p.14Chapter 2.2.1 --- Max-Product BP on MRFs --- p.14Chapter 2.2.2 --- Sum-Product BP on MRFs --- p.15Chapter 3 --- Our Formulation --- p.17Chapter 3.1 --- An MRF Model --- p.18Chapter 3.2 --- Coarse-to-Fine Optimization by BP --- p.21Chapter 3.2.1 --- Coarse-Level Belief Propagation --- p.23Chapter 3.2.2 --- Fine-Level Belief Propagation --- p.24Chapter 3.2.3 --- Performance Enhancement --- p.25Chapter 4 --- Experiments --- p.27Chapter 4.1 --- Comparison --- p.27Chapter 4.2 --- Failure Case --- p.32Chapter 5 --- Conclusion --- p.35Bibliography --- p.3