# Multi-Scale Detail-Noise Complementary Learning for Image Denoising
###### Abstract
Although the denoising algorithms described above have achieved good performance, most denoising methods fuse different levels of features through long and short skip connections, easily generating a lot of redundant information, thereby weakening the complementarity of different levels of features, resulting in the loss of image details [11]. Therefore, effectively retaining the detail features of an image poses a challenge in denoising tasks. To address these limitations, we propose a new multi-scale detail-noise complementary learning (MDNCL) network for AWGN and real-world noise removal. Our approach involves several steps. First, we employ a low-pass filter to extract a base layer and a detail layer from the input image. The detail layer contains edge and texture features, along with the noise mappings, while the base layer represents the approximate features of the image. The MDNCL network consists of two branches, namely the detail feature learning branch (DLB) and the noise learning branch (NLB). The detail layer serves as the input for both branches to separately learn image detail features and noisy mappings. In the DLB, we utilize a 2D Discrete Cosine Transform (DCT) to transform the detail layer from the spatial domain to the frequency domain. We also introduce the feature subtraction unit (FSU) to obtain the difference detail features between network layers. These difference features are then fused to produce rich multi-scale difference features, enhancing the complementarity between different network layers. Finally, the learned image features are combined with the base layer to reconstruct the final denoised image. We train the MDNCL network using appropriate loss functions to achieve a balance between noise removal and detail preservation. The proposed MDNCL effectively retains more image details and yields favorable denoising performance. Overall, the contributions of the proposed MDNCL can be summarized as follows:  
1. A novel multi-scale detail-noise complementary learning (MDNCL) network is introduced for the removal of AWGN and real-world noise. The MDNCL network incorporates two branches that extract rich detail features, effectively striking a balance between noise reduction and detail retention. 2. Complementary learning is approached from two perspectives. Firstly, the MDNCL network facilitates the complementary learning of detail features and noise distribution, enabling more effective noise removal while preserving important image details. Secondly, the network facilitates the complementary learning of features between different network layers within the detail feature learning branch. 3. The proposed FSU is proven to be an effective component in the MDNCL network. The FSU captures and leverages difference features within and between network layers. By avoiding the redundancy in image features and enhancing the complementarity between different network layers, the FSU further enhances the denoising performance. The remainder of this paper is structured as follows. Section 2 presents a comprehensive review of related work. Section 3 introduces the proposed MDNCL approach in detail, explaining on its architecture and learning strategies. Section 4 provides an overview of the datasets used in our study, presents the implementation details, and reports the experimental results. Section 5 concludes the study and presents the key findings and contributions of our research.