Image restoration is a fundamental and challenging task in computer vision, where CNN-based frameworks demonstrate significant computational efficiency. However, previous CNN-based methods often face challenges in adequately restoring fine texture details, which are limited by the small receptive field of CNN structures and the lack of channel feature modeling. In this paper, we propose WaMaIR, which is a novel framework with a large receptive field for image perception and improves the reconstruction of texture details in restored images. Specifically, we introduce the Global Multiscale Wavelet Transform Convolutions (GMWTConvs) for expandding the receptive field to extract image features, preserving and enriching texture features in model inputs. Meanwhile, we propose the Mamba-Based Channel-Aware Module (MCAM), explicitly designed to capture long-range dependencies within feature channels, which enhancing the model sensitivity to color, edges, and texture information. Additionally, we propose Multiscale Texture Enhancement Loss (MTELoss) for image restoration to guide the model in preserving detailed texture structures effectively. Extensive experiments confirm that WaMaIR outperforms state-of-the-art methods, achieving better image restoration and efficient computational performance of the model.

翻译：图像复原是计算机视觉领域一项基础且具有挑战性的任务，基于CNN的框架在此任务中展现出显著的计算效率优势。然而，以往的CNN方法在充分恢复精细纹理细节方面常面临挑战，这受限于CNN结构较小的感受野以及缺乏通道特征建模能力。本文提出WaMaIR，这是一种具有大感受野的新型图像感知框架，旨在提升复原图像中纹理细节的重建质量。具体而言，我们引入全局多尺度小波变换卷积（GMWTConvs），通过扩展感受野来提取图像特征，并在模型输入中保留和丰富纹理特征。同时，我们提出基于Mamba的通道感知模块（MCAM），该模块专门设计用于捕获特征通道内的长程依赖关系，从而增强模型对颜色、边缘及纹理信息的敏感性。此外，我们针对图像复原任务提出多尺度纹理增强损失（MTELoss），以有效指导模型保留细节纹理结构。大量实验证实，WaMaIR在图像复原效果和模型计算效率方面均优于现有先进方法。