Image Restoration Based on End-to-End Unrolled Network

Recent studies on image restoration (IR) methods under unrolled optimization frameworks have shown that deep convolutional neural networks (DCNNs) can be implicitly used as priors to solve inverse problems. Due to the ill-conditioned nature of the inverse problem, the selection of prior knowledge is crucial for the process of IR. However, the existing methods use a fixed DCNN in each iteration, and so they cannot fully adapt to the image characteristics at each iteration stage. In this paper, we combine deep learning with traditional optimization and propose an end-to-end unrolled network based on deep priors. The entire network contains several iterations, and each iteration is composed of analytic solution updates and a small multiscale deep denoiser network. In particular, we use different denoiser networks at different stages to improve adaptability. Compared with a fixed DCNN, it greatly reduces the number of computations when the total parameters are equal and the number of iterations is the same, but the gains from a practical runtime are not as significant as indicated in the FLOP count. The experimental results of our method of three IR tasks, including denoising, deblurring, and lensless imaging, demonstrate that our proposed method achieves state-of-the-art performances in terms of both visual effects and quantitative evaluations.