Toward Convolutional Blind Denoising of Real Photographs

TL;DR

This paper introduces CBDNet, a convolutional neural network designed for blind denoising of real photographs, trained on realistic noise models and real-world data, outperforming existing methods in quality and robustness.

Contribution

The paper proposes CBDNet, a novel CNN architecture with a noise estimation subnetwork, trained on realistic noise models and real data, improving denoising performance on real-world images.

Findings

CBDNet outperforms state-of-the-art methods on real-world datasets.

Incorporating realistic noise models enhances generalization.

The noise estimation subnetwork effectively suppresses under-estimation of noise.

Abstract

While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs. The main reason is that their learned models are easy to overfit on the simplified AWGN model which deviates severely from the complicated real-world noise model. In order to improve the generalization ability of deep CNN denoisers, we suggest training a convolutional blind denoising network (CBDNet) with more realistic noise model and real-world noisy-clean image pairs. On the one hand, both signal-dependent noise and in-camera signal processing pipeline is considered to synthesize realistic noisy images. On the other hand, real-world noisy photographs and their nearly noise-free counterparts are also included to train our CBDNet. To further provide an interactive strategy to rectify denoising result conveniently, a noise estimation subnetwork with asymmetric learning to suppress under-estimation of noise level is embedded into CBDNet. Extensive experimental results on three datasets of real-world noisy photographs clearly demonstrate the superior performance of CBDNet over state-of-the-arts in terms of quantitative metrics and visual quality. The code has been made available at https://github.com/GuoShi28/CBDNet.