DBDnet: A Deep Boosting Strategy for Image Denoising

In this paper, we propose a new deep network architecture named deep boosting denoising net (DBDnet) for image denoising. It is a residual learning network that can generate a noise map from a noisy observation. In detail, it first generates a coarse noise map via a simple structure, and then updates the noise map gradually via a boosting function. The motivation of our DBDnet stems from the observation that the noise map recovered by any algorithm cannot ideally equal the ground-truth noise map, which typically contains noise. We call this noise NoN, i.e., noise of noise map. Based on this observation, we formulate the denoising as a process of reducing NoN, and the role of DBDnet is to eliminate the NoN from the coarse noise map. In particular, we analyze the process of reducing NoN theoretically, and propose an NoN eliminating module to simulate it accordingly. We evaluate the proposed DBDnet on images polluted by different levels of additive white Gaussian noise and real noise. Experiment results demonstrate that our DBDnet can attain better denoising performance compared with state-of-the-art methods on several kinds of image denoising tasks. In particular, for the Gaussian denoising and real image denoising tasks, the average improvements of the PSNR values brought by our DBDnet are about 0.25 dB and 1.01 dB, respectively. In addition, we find and verify that the deep boosting insight can be easily introduced into the state-of-the-art image denoising network, and promotes its denoising performance. Our code is publicly available at https://github.com/jiayi-ma/DBDNet.