Efficient Non-Local Image Denoising Using Binary Descriptor Classification

Abstract

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 the 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.

Optimal Binary Descriptor for Pre-Classification in Efficient Non-Local Mean Image Denoising

“…Based on previous experimental results (25) , T h is set to 0.5 and we called this descriptor "simple binary pattern" (SBP).…”

“…Although preliminary study (25) shows that 12-neighbor and 16-neighbor SBP's produce better results than other descriptors, there are only a very limited number of descriptors being tested in the study. In order to comprehensively understand the behavior of SBP, we tested five kinds of SBP based on the number of neighbors being included in the description.…”

“…However, additional statistical parameters such as means or gradients need to be calculated before the NLM process and this adds additional complexity in the algorithm. In a preliminary study (25) , we have shown that using binary descriptors in the pre-classification can significantly improve NLM in both reducing computation time and enhancing denoising performance. A binary descriptor of an image patch compares the pixels in the patch with a given threshold and produces a simple binary string to describe the patch.…”