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Volume: 33 | Article ID: art00005
Decision-making on image denoising expedience
  DOI :  10.2352/ISSN.2470-1173.2021.10.IPAS-237  Published OnlineJanuary 2021

Image denoising is a classical preprocessing stage used to enhance images. However, it is well known that there are many practical cases where different image denoising methods produce images with inappropriate visual quality, which makes an application of image denoising useless. Because of this, it is desirable to detect such cases in advance and decide how expedient is image denoising (filtering). This problem for the case of wellknown BM3D denoiser is analyzed in this paper. We propose an algorithm of decision-making on image denoising expedience for images corrupted by additive white Gaussian noise (AWGN). An algorithm of prediction of subjective image visual quality scores for denoised images using a trained artificial neural network is proposed as well. It is shown that this prediction is fast and accurate.

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Andrii Rubel, Oleksii Rubel, Vladimir Lukin, Karen Egiazarian, "Decision-making on image denoising expediencein Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Processing: Algorithms and Systems XIX,  2021,  pp 237-1 - 237-7,

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