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Learning-Based Noise Component Map Estimation for Image Denoising

作     者:Bahnemiri, Sheyda Ghanbaralizadeh Ponomarenko, Mykola Egiazarian, Karen 

作者机构:Tampere Univ Tampere 33100 Finland 

出 版 物:《IEEE SIGNAL PROCESSING LETTERS》 (IEEE Signal Process Lett)

年 卷 期:2022年第29卷

页      面:1407-1411页

核心收录:

学科分类:0808[工学-电气工程] 08[工学] 

主  题:Estimation Training Noise measurement Noise reduction Image denoising Image color analysis Convolutional neural networks Image denoising non i i d noise blind noise parameters estimation deep convolutional neural networks 

摘      要:A problem of image denoising, when images are corrupted by a non-stationary noise, is considered in this paper. Since, in practice, no a priori information on noise is available, noise statistics should be pre-estimated prior to image denoising. In this paper, deep convolutional neural network (CNN) based method for estimation of a map of local, patch-wise, standard deviations of noise (so-called sigma-map) is proposed. It achieves the state-of-the-art performance in accuracy of estimation of sigma-map for the case of non-stationary noise, as well as estimation of a noise variance for the case of an additive white Gaussian noise. Extensive experiments on image denoising using estimated sigma-maps demonstrate that our method outperforms recent CNN-based blind image denoising methods by up to 6 dB in PSNR, as well as other state-of-the-art methods based on sigma-map estimation by up to 0.5 dB, providing, at the same time, better usage flexibility. A comparison with the ideal case, when denoising is applied using ground-truth sigma-map, shows that a difference of corresponding PSNR values for the most of noise levels is within 0.1-0.2 dB, and does not exceed 0.6 dB.

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