Learning-based Noise Component Map Estimation for Image Denoising
Ghanbaralizadeh Bahnemiri, Sheyda; Ponomarenko, Mykola; Egiazarian, Karen (2022)
Ghanbaralizadeh Bahnemiri, Sheyda
Ponomarenko, Mykola
Egiazarian, Karen
2022
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202208296798
https://urn.fi/URN:NBN:fi:tuni-202208296798
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [16951]