MUNet: A lightweight Mamba-based Under-Display Camera restoration network

Abstract

Under-Display Camera (UDC) restoration aims to recover the underlying clean images from the degraded images captured by UDC. Although promising results have been achieved, most existing UDC restoration methods still suffer from two vital obstacles in practice: (i) existing UDC restoration models are parameter-intensive, and (ii) most of them struggle to capture long-range dependencies within high-resolution images. To overcome above drawbacks, we study a challenging problem in UDC restoration, namely, how to design a lightweight UDC restoration model that could capture long-range image dependencies. To this end, we propose a novel lightweight Mamba-based UDC restoration network (MUNet) consisting of two modules, named Separate Multi-scale Mamba (SMM) and Separate Convolutional Feature Extractor (SCFE). Specifically, SMM exploits our proposed alternate scanning strategy to efficiently capture long-range dependencies across multi-scale image features. SCFE preserves local dependencies through convolutions with various receptive fields. Thanks to SMM and SCFE, MUNet achieves state-of-the-art lightweight UDC restoration performance with significantly fewer parameters, making it well-suited for deployment on mobile devices. Our codes will be available after acceptance.