Image deblocking via sparse representation |
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| Authors: | Cheolkon Jung Licheng Jiao Hongtao Qi Tian Sun |
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| Affiliation: | 1. Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai 200240, PR China;2. KU Leuven, ESAT-STADIUS, Leuven B-3001, Belgium;3. Shanghai Key Laboratory for Contemporary Applied Mathematics and School of Mathematical Sciences, Fudan University, Shanghai 200433, PR China;4. Department of Computational Mathematics, Science and Engineering and Department of Mathematics, Michigan State University, MI 48824, USA;1. The Chinese University of Hong Kong, Shatin, N.T., Hong Kong, China;2. South China University of Technology, Guangzhou, China;3. University of Electronic Science and Technology of China, Chengdu, China;4. Huawei Technologies Noah’s Ark Lab, Shenzhen, China;1. School of Mathematical Sciences/Research Center for Image and Vision Computing, University of Electronic Science and Technology of China, Chengdu 611731, PR China;2. Department of Mathematical Sciences, University of Texas at Dallas, Dallas, TX 75080, USA;1. Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China;2. Department of Electrical and Computer Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada |
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| Abstract: | Image compression based on block-based Discrete Cosine Transform (BDCT) inevitably produces annoying blocking artifacts because each block is transformed and quantized independently. This paper proposes a new deblocking method for BDCT compressed images based on sparse representation. To remove blocking artifacts, we obtain a general dictionary from a set of training images using the K-singular value decomposition (K-SVD) algorithm, which can effectively describe the content of an image. Then, an error threshold for orthogonal matching pursuit (OMP) is automatically estimated to use the dictionary for image deblocking by the compression factor of compressed image. Consequently, blocking artifacts are significantly reduced by the obtained dictionary and the estimated error threshold. Experimental results indicate that the proposed method is very effective in dealing with the image deblocking problem from compressed images. |
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