Learning Multi-scale Spatial-frequency Features for Image Denoising

• URL: http://arxiv.org/abs/2506.16307v1
• Date: Thu, 19 Jun 2025 13:28:09 GMT
• Title: Learning Multi-scale Spatial-frequency Features for Image Denoising
• Authors: Xu Zhao, Chen Zhao, Xiantao Hu, Hongliang Zhang, Ying Tai, Jian Yang,
• Abstract summary: We propose a novel multi-scale adaptive dual-domain network (MADNet) for image denoising.<n>We use image pyramid inputs to restore noise-free results from low-resolution images.<n>In order to realize the interaction of high-frequency and low-frequency information, we design an adaptive spatial-frequency learning unit.
• Score: 58.883244886588336
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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