Advanced Reference-Constrained Image Restoration Algorithm

Image restoration is always a long-term problem in low-level computer vision, which has many practical applications. In this paper, we devote to reconstructing a single image from compressive measurements with the assistance of one or multiple reference images, by using the similarity prior information of them. More specifically, we propose to bring in the concept of Regularization by Denoising, which leverages existing denoising engine to regularize inverse problems, and illustrate how the reference images and this powerful algorithm can be merged into a new highly effective recovery approach, which is named Reference Image Constrained Regularization by Denoising for solving various image restoration tasks. Since the similarity between the reference and the ground truth is not guaranteed, or the degree of the similarity is different, we extend the proposed algorithm by adding adaptive weighting matrix to the difference between the target image and the reference images to further improve the performance. We evaluate experimentally the proposed algorithm in the image deblurring and single image super-resolution problems. Furthermore, we also carry out our method on video super-resolution task, in the case of multiple reference images available. We compare the advanced reference-constrained image restoration algorithm with previous methods about reconstruction accuracy and computation cost. The experimental results show that the proposed method outperforms state-of-the-art algorithms, including Regularization by Denoising, in terms of both the Peak Signal to Noise Ratio and the visual performance.