No-reference image noise estimation based on noise level accumulation

In this paper, a method of no-reference image noise assessment is presented, which utilizes the estimated noise level accumulation (NLA) index value. The affine reconstruction model is applied after segmenting the noisy image into several patches. Boundary blur process is conducted to smooth the segmentation edges. For each image patch the mean value standing for brightness and the standard deviation value indicating the noise standard deviation are computed to give the noise samples estimation. The accurate image noise standard deviation is estimated by integrating NLA index value of several overlapped intervals combined with different visual weights. Experiment results are provided to demonstrate that the proposed method performs well for images with different contents over a large range of noise levels both monotonously and accurately. Comparisons against other conventional approaches are also carried out to exhibit the superior performance of the proposed algorithm.