VmambaIR: Visual State Space Model for Image Restoration

Image restoration is a critical task in low-level computer vision, aiming to restore high-quality images from degraded inputs. Various models, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), transformers, and diffusion models (DMs), have been employed to address this problem with significant impact. However, CNNs have limitations in capturing long-range dependencies. DMs require large prior models and computationally intensive denoising steps. Transformers have powerful modeling capabilities but face challenges due to quadratic complexity with input image size. To tackle these challenges, we propose VmambaIR, one of the first works to introduce State Space Models (SSMs)…

• NN
  National Natural Science Foundation of China

  Awards: 62171251, 62311530100, KJZD20231023094700001, Nos.62171251&62311530100