FSI: Frequency and Spatial Interactive Learning for Image Restoration in Under-Display Cameras

Chengxu Liu, Xuan Wang, Shuai Li, Yuzhi Wang, Xueming Qian; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2023, pp. 12537-12546

Under-display camera (UDC) systems remove the screen notch for bezel-free displays and provide a better interactive experience. The main challenge is that the pixel array of light-emitting diodes used for display diffracts and attenuates the incident light, leading to complex degradation. Existing models eliminate spatial diffraction by maximizing model capacity through complex design and ignore the periodic distribution of diffraction in the frequency domain, which prevents these approaches from satisfactory results. In this paper, we introduce a new perspective to handle various diffraction in UDC images by jointly exploring the feature restoration in the frequency and spatial domains, and present a Frequency and Spatial Interactive Learning Network (FSI). It consists of a series of well-designed Frequency-Spatial Joint (FSJ) modules for feature learning and a color transform module for color enhancement. In particular, in the FSJ module, a frequency learning block uses the Fourier transform to eliminate spectral bias, a spatial learning block uses a multi-distillation structure to supplement the absence of local details, and a dual transfer unit to facilitate the interactive learning between features of different domains. Experimental results demonstrate the superiority of the proposed FSI over state-of-the-art models, through extensive quantitative and qualitative evaluations in three widely-used UDC benchmarks.