The purpose of this paper is to introduce a fast automated whitenoise estimation method which gives reliable estimates in images with smooth and textured areas. This method is a block-based method that takes image structure into account and uses a measure other than the variance to determine if a block is homogeneous. It uses no thresholds and automates the way that blockbased methods stop the averaging of block variances. The proposed method selects intensity-homogeneous blocks in an image by rejecting blocks ofstructure using a new structure analyzer. The analyzer used is based on high-pass operators and special masks for corners to allow implicit detection of structure and to stabilize the homogeneity estimation. For typical image quality (PSNR of 2040 dB) the proposed method outperforms other methods significantly and the worst-case estimation error is 3 dB which is suitable for real applications such as video suweillance or broadcasts. The method performs well even in images with few smooth areas and in highly noisy images.
Abstract-Noise can significantly impact the effectiveness of video processing algorithms. This paper proposes a fast white-noise variance estimation that is reliable even in images with large textured areas. This method finds intensity-homogeneous blocks first and then estimates the noise variance in these blocks, taking image structure into account. This paper proposes a new measure to determine homogeneous blocks and a new structure analyzer for rejecting blocks with structure. This analyzer is based on high-pass operators and special masks for corners to stabilize the homogeneity estimation. For typical video quality (PSNR of 20-40 dB), the proposed method outperforms other methods significantly and the worst-case estimation error is 3 dB, which is suitable for real applications such as video broadcasts. The method performs well both in highly noisy and good-quality images. It also works well in images including few uniform blocks.Index Terms-Adaptive variance averaging, homogeneous regions, second-order operators, structure analyzers, textured regions, video enhancement, video noise estimation, white noise.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.