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Image Denoising Based Sparse Representation and Dictionary Learned

Author: JiangPengFei
Tutor: WangWeiWei
School: Xi'an University of Electronic Science and Technology
Course: Computational Mathematics
Keywords: Denoising Sparse Representation Dictionary to learn Block coordinate relaxation algorithm
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 598
Quote: 1
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Abstract


Image in the process of acquisition, storage, transmission , will be subject to the specific noise pollution , resulting in a drop in image quality , image denoising is an important problem in image processing . Its purpose is possible to eliminate noise by a certain processing , improve image quality . In recent years, the sparse representation theory is attracting widespread attention, and successfully applied to image denoising . Its theoretical basis , clean image with a certain smoothness sparse representation , can achieve the purpose of de-noising select or design the appropriate dictionary , find the image in the dictionary under the sparse overcomplete dictionary . Image denoising applications , the choice of the dictionary : one is to select a fixed analytic dictionary ; another sampled image data through appropriate models and methods of learning or training adaptive dictionary . Since learning has a self- adaptive data dictionary , the essential characteristics of the data to better characterize the denoising applications to get better results. This article draw on the idea of the K-SVD algorithm , improved dictionary learning algorithm based on orthonormal basis of joint predecessors . This algorithm is improved in the process of updating the dictionary updated in real coefficient, to improve the speed of a dictionary learning ; adaptive dictionary using a structure obtained by this algorithm (L-ONB dictionary) and used for image denoising , experimental results show that relative to the fixed dictionary , improved learning dictionary denoising algorithm can get better denoising effect .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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