Image Denoising Using Quadtree Based Nonlocal Means With Locally Adaptive Principal Component Analysis
In this letter, we present an efficient image denoising method combining quadtree based nonlocal means (NLM) and locally adaptive principal component analysis. It exploits nonlocal multiscale self similarity better, by creating sub patches of different sizes using quadtree decomposition on each patch. To achieve spatially uniform denoising, we propose a local noise variance estimator combined with denoiser based on locally adaptive principal component analysis. Experimental results demonstrate that our proposed method achieves very competitive denoising performance compared with state of the art denoising methods, even obtaining better visual perception at high noise levels.
IEEE Signal Processing Letters