提出多尺度自适应双域网络，更好分离高频与低频噪声。
Learning Multi-scale Spatial-frequency Features for Image Denoising
- 用图像金字塔输入，分层恢复图像细节
- 设计可学习掩码分离高低频信息，提升去噪精度
- 适合处理真实噪声图像，对低频/高频噪声均有效
近期多尺度架构在图像去噪任务中表现优异。然而，现有方法主要依赖固定单输入单输出的Unet结构，忽略了像素级的多尺度表征。此外，以往方法对频率域处理过于均匀，未区分高低频噪声特性。本文提出一种新型多尺度自适应双域网络（MADNet）。通过图像金字塔输入，从低分辨率图像中恢复无噪声结果。为实现高低频信息交互，设计了自适应空间-频率学习单元（ASFU），使用可学习掩码将信息分离为高低频成分。在跳跃连接中引入全局特征融合块，增强多尺度特征表达。在合成与真实噪声图像数据集上的大量实验表明，MADNet优于当前主流去噪方法。
原文摘要 · Abstract (English)
Recent advancements in multi-scale architectures have demonstrated exceptional performance in image denoising tasks. However, existing architectures mainly depends on a fixed single-input single-output Unet architecture, ignoring the multi-scale representations of pixel level. In addition, previous methods treat the frequency domain uniformly, ignoring the different characteristics of high-frequency and low-frequency noise. In this paper, we propose a novel multi-scale adaptive dual-domain network (MADNet) for image denoising. We use image pyramid inputs to restore noise-free results from low-resolution images. In order to realize the interaction of high-frequency and low-frequency information, we design an adaptive spatial-frequency learning unit (ASFU), where a learnable mask is used to separate the information into high-frequency and low-frequency components. In the skip connections, we design a global feature fusion block to enhance the features at different scales. Extensive experiments on both synthetic and real noisy image datasets verify the effectiveness of MADNet compared with current state-of-the-art denoising approaches.
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