Probabilistic Non-Local Means
This is an incremental improvement for image processing researchers, enhancing denoising accuracy in noisy images.
The paper tackles image denoising by proposing a probabilistic non-local means (PNLM) method that addresses defects in the classic NLM weight function, using theoretical statistics for Gaussian noise to formulate probabilistic weights, resulting in improved performance over classic NLM and variants in PSNR and SSIM metrics.
In this paper, we propose a so-called probabilistic non-local means (PNLM) method for image denoising. Our main contributions are: 1) we point out defects of the weight function used in the classic NLM; 2) we successfully derive all theoretical statistics of patch-wise differences for Gaussian noise; and 3) we employ this prior information and formulate the probabilistic weights truly reflecting the similarity between two noisy patches. The probabilistic nature of the new weight function also provides a theoretical basis to choose thresholds rejecting dissimilar patches for fast computations. Our simulation results indicate the PNLM outperforms the classic NLM and many NLM recent variants in terms of peak signal noise ratio (PSNR) and structural similarity (SSIM) index. Encouraging improvements are also found when we replace the NLM weights with the probabilistic weights in tested NLM variants.