Multitask Learning Mechanism for Remote Sensing Image Motion Deblurring

As a fundamental preprocessing technique, remote sensing image motion deblurring is important for visual understanding tasks. Most conventional approaches formulate the image motion deblurring task as a kernel estimation. Because the kernel estimation is a highly ill-posed problem, many priors have been applied to model the images and kernels. Even though these methods have obtained relatively better performances, they are usually time-consuming and not robust for different conditions. To address this problem, we propose a multitask learning mechanism for remote sensing image motion deblurring in this article, which contains an image restoration subtask and an image texture complexity recognition one. First, we consider the image motion deblurring problem as a domain transformation problem, from the blurred domain to a clear one. Specifically, the blurred domain represents the data space consisted of blurring images, and the definition of clear domain is similar. Second, we design a novel weighted attention map loss to enhance the reconstruction capability of the restoration subbranch for difficult local regions. Third, based on the restoration subbranch, a recognition subbranch is incorporated into the framework to guide the deblurring process, which provides the auxiliary texture complexity information to help the optimization of restoration subbranch. Additionally, in order to optimize the proposed network, we construct three large-scale datasets, and each sample in the dataset contains a clear image, a blurred image, and its texture label obtained by corresponding texture complexity. Finally, the experimental results on three constructed datasets demonstrate the robustness and the effectiveness of the proposed method.