Halftoning-based Block Truncation Coding image restoration |
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| Affiliation: | 1. Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan;2. Faculty of Computer Science and Information Technology, University of Malaya, Kuala Lumpur, Malaysia;1. Department of Computer Science, City University of Hong Kong, Hong Kong;2. School of Information Science and Engineering, Shandong University, China;3. City University of Hong Kong Shenzhen Research Institute, Shenzhen, China;1. Electrical Engineering Department, Amirkabir University of Technology, Tehran, Iran;2. Electrical Engineering Department, Semnan University, Semnan, Iran;1. School of Computer Science and Technology, Xidian University, Xi’an 710071, Shaanxi, China;2. Xi’an Research Institute of Surveying and Mapping, Xi’an, Shaanxi, 710054, China;3. State Key Laboratory of Geo-Information Engineering, Xi’an, Shaanxi, 710054, China |
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| Abstract: | This paper presents a new image restoration method for improving the quality of halftoning-Block Truncation Coding (BTC) decoded image in a patch-based manner. The halftoning-BTC decoded image suffers from the halftoning impulse noise which can be effectively reduced and suppressed using the Vector Quantization (VQ)-based and sparsity-based approaches. The VQ-based approach employs the visual codebook generated from the clean image, whereas the sparsity-based approach utilizes the double learned dictionaries in the noise reduction. The sparsity-based approach assumes that the halftoning-BTC decode image and clean image share the same sparsity coefficient. In the sparse coding stage, it uses the halftoning-BTC dictionary, while in the reconstruction stage, it exploits the clean image dictionary. As suggested by the experimental results, the proposed method outperforms in the halftoning-BTC image reconstructed when compared to that of the filtering approaches. |
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| Keywords: | Halftoning Error diffusion Ordered dithering Block truncation coding Image restoration Sparse representation Image compression Vector quantization |
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