کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
529750 | 869697 | 2016 | 5 صفحه PDF | دانلود رایگان |
• A new image restoration method for improving the quality of halftoning-BTC images.
• The sparsity-based approach utilizes the double learned dictionaries in the noise reduction.
• Experimental results demonstrate that the proposed method is superior to former schemes.
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.
Journal: Journal of Visual Communication and Image Representation - Volume 35, February 2016, Pages 193–197