Abstract
How to preserve the edge and texture details has been a difficult problem in image denoising. In this paper, we propose a multi-layer prior information learning method, which combines the statistical and geometric features of the image to describe the attributes of the prior information more accurately and completely. The experimental results show that our proposed method is superior to the EPLL (Expected patch log likelihood) method with a single statistical characteristic for a priori learning in both visual and quantitative evaluation.
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Wang, S., Xie, J., Zheng, Y., Jiang, T., Xue, S. (2018). Expected Patch Log Likelihood Based on Multi-layer Prior Information Learning. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_49
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DOI: https://doi.org/10.1007/978-981-10-7605-3_49
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