Learning-based Noise Component Map Estimation for Image Denoising
| Autor: | Sheyda Ghanbaralizadeh Bahnemiri, Mykola Ponomarenko, Karen Egiazarian |
|---|---|
| Přispěvatelé: | Tampere University, Computing Sciences |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Applied Mathematics Computer Science::Computer Vision and Pattern Recognition Computer Vision and Pattern Recognition (cs.CV) Signal Processing Image and Video Processing (eess.IV) Computer Science - Computer Vision and Pattern Recognition FOS: Electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Electrical Engineering and Systems Science - Image and Video Processing 113 Computer and information sciences Machine Learning (cs.LG) |
| Popis: | 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 for 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 noise variance for the case of 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 same time better usage flexibility. Comparison with the ideal case, when denoising is applied using ground-truth sigma-map, shows that a difference of corresponding PSNR values for most of noise levels is within 0.1-0.2 dB and does not exceeds 0.6 dB. Comment: 5 pages, submitted to IEEE Signal Processing Letters |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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