Enhancing Local-Global Representation Learning for Image Restoration

Vision systems are the core element in industrial systems, such as intelligent transportation systems and inspection robots. However, undesired degradations caused by bad weather or low-end devices reduce the visibility of images. Image restoration aims to reconstruct a sharp image from a degraded counterpart and plays an important role in industrial systems. Recent transformer-based architectures leverage the self-attention unit and convolutions to model long-range dependencies and local connectivity, respectively, achieving promising performance for image restoration. However, these methods have quadratic complexity with respect to the input size. In addition, convolution operators are ineffective enough to recover the local details. This article presents a joint local and global representation learning framework for image restoration, called LoGoNet. Specifically, to enhance global contexts, we excavate the potential of pooling techniques to refine large-scale feature maps, which help handle large-size degradations. Furthermore, we develop a novel module to emphasize local edges with the implicit Laplace operator. With these designs, the proposed LoGoNet produces powerful feature representations for image restoration, which is helpful for perceiving objects of different sizes in industrial systems. Extensive experiments demonstrate that LoGoNet achieves state-of-the-art performance on nine datasets for four image restoration tasks: image defocus/motion deblurring, image dehazing, and image desnowing.