Learning-based Noise Component Map Estimation for Image Denoising

TL;DR

This paper introduces a deep learning method for estimating local noise levels in images, significantly improving denoising performance especially for non-stationary noise by accurately predicting sigma-maps.

Contribution

It proposes a CNN-based approach for local noise standard deviation estimation, achieving state-of-the-art accuracy and enhancing blind image denoising effectiveness.

Findings

Outperforms recent CNN-based blind denoising methods by up to 6 dB in PSNR.

Achieves near-ideal denoising performance when using ground-truth sigma-maps.

Provides flexible and accurate noise estimation for various noise types.

Abstract

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.