A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise

Aleksey S. Rubel; Vladimir V. Lukin; Karen O. Egiazarian

doi:10.1117/12.2082533

16 March 2015 A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise

Aleksey S. Rubel, Vladimir V. Lukin, Karen O. Egiazarian

Author Affiliations +

Proceedings Volume 9399, Image Processing: Algorithms and Systems XIII; 93990P (2015) https://doi.org/10.1117/12.2082533
Event: SPIE/IS&T Electronic Imaging, 2015, San Francisco, California, United States

Abstract

Results of denoising based on discrete cosine transform for a wide class of images corrupted by additive noise are obtained. Three types of noise are analyzed: additive white Gaussian noise and additive spatially correlated Gaussian noise with middle and high correlation levels. TID2013 image database and some additional images are taken as test images. Conventional DCT filter and BM3D are used as denoising techniques. Denoising efficiency is described by PSNR and PSNR-HVS-M metrics. Within hard-thresholding denoising mechanism, DCT-spectrum coefficient statistics are used to characterize images and, subsequently, denoising efficiency for them. Results of denoising efficiency are fitted for such statistics and efficient approximations are obtained. It is shown that the obtained approximations provide high accuracy of prediction of denoising efficiency.

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Aleksey S. Rubel, Vladimir V. Lukin, and Karen O. Egiazarian "A method for predicting DCT-based denoising efficiency for grayscale images corrupted by AWGN and additive spatially correlated noise", Proc. SPIE 9399, Image Processing: Algorithms and Systems XIII, 93990P (16 March 2015); https://doi.org/10.1117/12.2082533

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