RestorMamba: An Enhanced Synergistic State Space Model for Image Restoration
Zeyu Wang, Chen Li, Huiying Xu, Xinzhong Zhu, Xiao Xian Huang, Hongbo Li · 2025
In this paper, we introduce an image inpainting method based on the State Space Model (SSM), named Restoration Mamba (RestorMamba). This approach incorporates effi-cient long-range dependency modeling within the network, which is particularly suited for the complexities of high-texture and high-resolution image restoration scenarios. To benefit from a broader context while maintaining global receptive fields, we have designed two pivotal modules: Skip Scan and Enhanced Synergistic Mamba (ESM) Block. Our experimental results demonstrate that RestorMamba achieves state-of-the-art performance in tasks such as image deraining and image denoising, encompassing Gaussian grayscale / color denoising and real image denoising.