A separation–aggregation network for image denoising

Image denoising is the problem that aims at recovering a clean image from a noisy counterpart. A promising solution for image denoising is to employ an appropriate deep neural network to learn a hierarchical mapping function from the noisy image to its clean counterpart. This mapping function, however, is generally difficult to learn since the potential feature space of the noisy patterns can be huge. To overcome this difficulty, we propose a separation–aggregation strategy to decompose the noisy image into multiple bands, each of which exhibits one kind of pattern. Then a deep mapping function is learned for each band and the mapping results are ultimately assembled to the clean image. By doing so, the network only needs to deal with the compositing components of the noisy image, thus makes it easier to learn an effective mapping function. Moreover, as any image can be viewed as a composition of some basic patterns, our strategy is expected to better generalize to unseen images. Inspired by this idea, we develop a separation–aggregation network. The proposed network consists of three blocks, namely a convolutional separation block that decomposes the input into multiple bands, a deep mapping block that learns the mapping function for each band, and a band aggregation block that assembles the mapping results. Experimental results demonstrate the superiority of our strategy over counterparts without image decomposition.