Denoising Real Bursts with Squeeze-and-excitation Residual Network

The goal of image denoising is to recover a clean image from noisy input(s). For single image denoising, utilising similarities (or priors) within and across an image dataset helps recover clean images. As the noise level increases, using multiple frames become feasible, which is defined as burst denoising. In this study, the authors propose a deep residual model with squeeze-and-excitation (SE) modules for the burst denoising. Unlike previous methods, the authors' model does not need an explicit aligning procedure, which is light-weighted and fast. The network contains a noise estimation convolutional neural network, which makes it capable of blind denoising. Besides, by inverting the image processing pipeline and simulating real noise in bursts, their model can suppress real noise blindly. Since denoising performance is closely related to the noise level, frame displacement, and the number of frames (burst length), intensive experiments including ablation study are performed. Quantitative results show that the proposed method performs significantly better than previous state-of-the-art methods V-BM4D and KPN in removing Gaussian noise. Qualitative results show that the proposed method is also effective in removing real noise using bursts and the SE module is key to reduce blur in results.