Noise variance estimation in nonlocal transform domain
Aram Danielyan, Alessandro Foi · 2009
We consider the estimation of the variance of an additive white Gaussian noise corrupting an image. In the proposed approach, we exploit the nonlocal self-similarity of images to achieve an improved separation of noise and signal. In particular, we utilize the same adaptive 3-D transform decomposition used in the BM3D (block-matching and 3-D filtering) denoising algorithm, where mutually similar blocks are stacked together and jointly processed. An adaptive-size portion of the high-frequency ends of the 3-D transform spectra is retained and used as input sample for a robust median estimator of the absolute deviation. Experimental analysis demonstrate a state-of-the-art accuracy of the proposed approach.