论文标题
sumd：超级U形矩阵分解卷积神经网络，用于图像denoising
SUMD: Super U-shaped Matrix Decomposition Convolutional neural network for Image denoising
论文作者
论文摘要
在本文中，我们提出了一个基于CNN的新颖而高效的框架，该框架利用本地和全球上下文信息进行图像降级。由于卷积本身的局限性，基于CNN的方法通常无法构建有效且结构化的全局特征表示形式，通常称为基于变压器方法中的长距离依赖关系。为了解决此问题，我们在网络中介绍了矩阵分解模块（MD），以建立全局上下文功能，与基于变压器的方法性能相当。受U形体系结构多阶段渐进式恢复的设计的启发，我们将MD模块进一步集成到多支车中，以在当前阶段获取斑块范围的相对全局特征表示。然后，阶段输入逐渐上升到整体范围，并不断提高最终功能。各种图像降级数据集的实验结果：SIDD，DND和Synthetic Gaussian噪声数据集表明，我们的模型（SUMD）可以通过基于变压器的方法产生可比的视觉质量和准确性结果。
In this paper, we propose a novel and efficient CNN-based framework that leverages local and global context information for image denoising. Due to the limitations of convolution itself, the CNN-based method is generally unable to construct an effective and structured global feature representation, usually called the long-distance dependencies in the Transformer-based method. To tackle this problem, we introduce the matrix decomposition module(MD) in the network to establish the global context feature, comparable to the Transformer based method performance. Inspired by the design of multi-stage progressive restoration of U-shaped architecture, we further integrate the MD module into the multi-branches to acquire the relative global feature representation of the patch range at the current stage. Then, the stage input gradually rises to the overall scope and continuously improves the final feature. Experimental results on various image denoising datasets: SIDD, DND, and synthetic Gaussian noise datasets show that our model(SUMD) can produce comparable visual quality and accuracy results with Transformer-based methods.
