Abstract:
Many non-local means (NLM) methods either use Euclidean distance to measure patch similarity, compute weight ij only once and keep it unchanged during denoising iterations, or use only the structure information of the denoised image to update weight.
These may limit denoising. This paper proposes NLAM image denoising to address these issues. NLAM iteratively updates weight_ij_as an optimization variable. Pixel-pixel, patch-patch, and coupled unbiased distances follow.
These unbiased distances measure image pixel/patch similarity better than Euclidean distance. The UD-NLAM uses the coupled unbiased distance. To accommodate different noise levels, we use multipatch UD-NLAM (MUD-NLAM).
We then propose MUD-NLAM with wavelet shrinkage (MUD-NLAM-WS) to improve denoising. Experimental results show that NLAM, UD-NLAM, and MUD-NLAM outperform existing NLM methods, and MUDNLAM-WS outperforms state-of-the-art denoising methods.
Note: Please discuss with our team before submitting this abstract to the college. This Abstract or Synopsis varies based on student project requirements.
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