Efficient Non-Local Image Denoising Using Binary Descriptor Classification

Jan-Ray Liao, Ya-Wei Tsai, Yu-Hsin Huang
2016 The Proceedings of the 4th International Conference on Industrial Application Engineering 2016   unpublished
Non-local mean (NLM) is one of the most effective image denoising methods currently available. It calculates weights of neighboring pixels based on the similarity between two image patches. The pixel is then estimated by the weighted sum of the neighboring pixels and itself. Because the large number of the patches needs to be compared, NLM incurs extremely high computational cost. In this paper, we propose to use binary descriptors to reject dissimilar patches from the computation and improve
more » ... e performance of NLM. A binary descriptor of a patch compares the pixels in the patch with a given threshold and produces a simple binary string to describe the patch. It is very simple to generate and the comparison between two descriptors is computationally efficient. Experimental results show that using a simple binary descriptor can effectively increase the denoising performance of NLM and significantly reduce the execution time.
doi:10.12792/iciae2016.056 fatcat:plqnfn6asjeftg6vn4hazo2lnu