Block-based noise estimation using adaptive Gaussian filtering

Abstract

This paper proposes a fast noise estimation algorithm using a Gaussian filter. It is based on block-based noise estimation, in which an input image is assumed to be contaminated by the additive white Gaussian noise and a filtering process is performed by an adaptive Gaussian filter. Coefficients of a Gaussian filter are selected as functions of the standard deviation of the Gaussian noise that is estimated from an input noisy image. For estimation of the amount of noise (i.e., standard deviation of the Gaussian noise), we split an image into a number of blocks and select smooth blocks that are classified by the standard deviation of intensity of a block, where the standard deviation is computed from the difference of the selected block images between the noisy input image and its filtered image. In experiments, the performance of the proposed algorithm is compared with that of the three conventional (block-based and filtering-based) noise estimation methods. Experiments with several still images show the effectiveness of the proposed algorithm. The proposed noise estimation algorithm can be efficiently applied to noise reduction in commercial image- or video-based applications such as digital cameras and digital television (DTV) for its performance and simplicity.