Blockwise SURE Shrinkage for Non-Local Means
This work addresses image denoising for applications like photography or medical imaging, but it is incremental as it builds on existing non-local means methods.
The paper tackles the shrinkage problem for non-local means image denoising by deriving a closed-form optimal blockwise shrinkage that minimizes Stein's unbiased risk estimator, resulting in improved performance with higher PSNR and SSIM and increased robustness against parameter changes.
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.