Noise estimation using adaptive Gaussian filtering and variable block size image segmentation

This paper proposes an efficient algorithm for noise level estimation in still images. The images are assumed to be corrupted by additive white Gaussian noise. The proposed method relies on block-based image segmentation and Gaussian filtering to estimate the standard deviation of Gaussian noise. The proposed method employs adaptive image segmentation, where the size of segmentation blocks is derived from the initial estimates of noise standard deviation. Although image segmentation is a two-stage process that allows forming local noise level estimates from very small image patches, specific measures have been taken to improve computational efficiency of the proposed method. Image prefiltering is also adaptive in a sense that the coefficients of a Gaussian filter are evaluated as a function of the initial noise level estimate. The proposed method is designed to reduce the likelihood of underestimation of noise level due to intensity clipping. The results obtained on database of natural images show that the proposed scheme can accurately estimate the noise variance for a wide range of noise levels from very small to very high noise levels. In addition, it has been demonstrated that the proposed image segmentation scheme offers an accurate and consistent estimation of homogenous image patches.