NERNet: Noise Estimation and Removal Network for Image Denoising

Abstract While some denoising methods based on deep learning achieve superior results on synthetic noise, they are far from dealing with photographs corrupted by realistic noise. Denoising on real-world noisy images faces more significant challenges due to the source of it is more complicated than synthetic noise. To address this issue, we propose a novel network including noise estimation module and removal module (NERNet). The noise estimation module automatically estimates the noise level map corresponding to the information extracted by symmetric dilated block and pyramid feature fusion block. The removal module focuses on removing the noise from the noisy input with the help of the estimated noise level map. Dilation selective block with attention mechanism in the removal module adaptively not only fuses features from convolution layers with different dilation rates, but also aggregates the global and local information, which is benefit to preserving more details and textures. Experiments on two datasets of synthetic noise and three datasets of realistic noise show that NERNet achieves competitive results in comparison with other state-of-the-art methods.