Blockwise SURE Shrinkage for Non-Local Means

TL;DR

This paper introduces a closed-form solution for optimal blockwise shrinkage in non-local means image denoising, enhancing performance and robustness by minimizing Stein's unbiased risk estimator.

Contribution

It derives a novel closed-form for blockwise shrinkage in NLM and proposes a fast algorithm, improving denoising quality and parameter robustness.

Findings

Increased PSNR and SSIM in denoised images.

Enhanced robustness of NLM against parameter variations.

Fast algorithm with constant complexity.

Abstract

In this letter, we investigate the shrinkage problem for the non-local means (NLM) image denoising. In particular, we derive the closed-form of the optimal blockwise shrinkage for NLM that minimizes the Stein's unbiased risk estimator (SURE). We also propose a constant complexity algorithm allowing fast blockwise shrinkage. Simulation results show that the proposed blockwise shrinkage method improves NLM performance in attaining higher peak signal noise ratio (PSNR) and structural similarity index (SSIM), and makes NLM more robust against parameter changes. Similar ideas can be applicable to other patchwise image denoising techniques.