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 address
these challenges, we propose VmambaIR, which introduces State Space Models
(SSMs) with linear complexity into comprehensive image restoration tasks. We
utilize a Unet architecture to stack our proposed Omni Selective Scan (OSS)
blocks, consisting of an OSS module and an Efficient Feed-Forward Network
(EFFN). Our proposed omni selective scan mechanism overcomes the unidirectional
modeling limitation of SSMs by efficiently modeling image information flows in
all six directions. Furthermore, we conducted a comprehensive evaluation of our
VmambaIR across multiple image restoration tasks, including image deraining,
single image super-resolution, and real-world image super-resolution. Extensive
experimental results demonstrate that our proposed VmambaIR achieves
state-of-the-art (SOTA) performance with much fewer computational resources and
parameters. Our research highlights the potential of state space models as
promising alternatives to the transformer and CNN architectures in serving as
foundational frameworks for next-generation low-level visual tasks.

我们提出了 VmambaIR，这是一种综合图像恢复任务的方法，它将线性复杂度的状态空间模型引入其中，通过有效地建模图像信息在各个方向上的流动来克服了状态空间模型的单向建模限制，并在图像去雨、单幅图像超分辨率和实际图像超分辨率等多个图像恢复任务上取得了最先进的性能。