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RDS-Denoiser: a Detail-preserving Convolutional Neural Network for Image Denoising

Image noise is usually modeled as additive independent Gaussian random variables with fixed standard deviation, and most existing methods developed under this assumption have difficulties handling spatially varying noise. In this work, we aim to solve the problem of image denoising when the noise level is unknown. We propose a simple yet effectively Stacked Denoising Networks. It decomposes the denosing process into two stages. The Sage-I Denosing is to predict the noisy map of noisy image. The Stage-II Denosing to further improve the visual quality and alleviate overfitting to Gaussian noise. Experiments show that RDS-Denoiser achieves competitive performance comparing to state-of-the-art denoising methods. In addition, we propose RDS-GAN, a conditional generative adversarial network, to further improve the visual quality and alleviate overfitting to Gaussian noise.