# Multi-Scale Detail-Noise Complementary Learning for Image Denoising
###### Abstract  
Deep convolutional neural networks (CNNs) have demonstrated significant potential in enhancing image denoising performance. However, 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. In this paper, we propose a multi-scale detail-noise complementary learning (MDNCL) network for additive white Gaussian noise removal and real-world noise removal. The MDNCL network comprises two branches, namely the Detail Feature Learning Branch (DLB) and the Noise Learning Branch (NLB). Specifically, a loss function is applied to guide the complementary learning of image detail features and noisy mappings in these two branches. This learning approach effectively balances noise reduction and detail restoration, especially when dealing with high ratios of noise. To enhance the complementarity of features between different network layers and avoid redundant information, we designed a Feature Subtraction Unit (FSU) to capture the differences in features across the DLB network layers. Our extensive experimental evaluations demonstrate that the MDNCL approach achieves impressive denoising performance and outperforms other popular denoising methods. image denoising: Gaussian noise; complementary learning: deep learning +
Footnote †: journal: Medical  
image denoising methods can deliver satisfactory denoising results, they often involve computationally expensive procedures and rely heavily on prior knowledge of the image. Compared to traditional denoising algorithms, deep convolutional neural networks (CNNs) have shown significant improvements in image denoising tasks by automatically learning network parameters from large datasets without relying on prior assumptions. DnCNN [9] generates residual images containing only noise through residual learning and improves the performance of the denoising network combined with batch normalization (BN). Based on the DnCNN model, [PERSON] et al. [10] successively proposed a fast and flexible denoising network (FFDNet). The input of FFDNet is noise-level estimation, which can solve the more complex real-world image denoising problem. However, as the depth of the network increases, the deep network layers may learn similar or duplicate feature representations, resulting in redundant features and degraded denoising performance [11]. Therefore, the researchers denoised images by increasing the width of the network or using skip join operations. [PERSON] et al. [12] proposed a residual dense neural network (RDUNet) based on a densely connected hierarchical network for image denoising. The encoding layer and decoding layer of RDUNet are composed of densely connected convolutional layers to reuse feature maps and local residual learning, avoid gradient disappearance, and speed up the learning process. [PERSON] et al. [13] proposed a simple and effective multiscale denoising back-projection feature fusion network to recover underwater images affected by complex degradation, which solves the limitations of traditional encoder-decoder methods in detail recovery and complex degradation processing. [PERSON] et al. [14] constructed a wide CNN (WCNN) framework to reorganize several convolutional layers. Enough convolutional layers are trained using different loss functions so that the WCNN becomes a truly wide network and all subnetworks are trained in parallel, which greatly shortens the training time. [PERSON] et al. [15] proposed a new dual convolutional neural network (DCANet) for blind image denoising, which is the first network to combine dual CNNs and attention mechanisms for image denoising. DCANet can suppress not only singular noise but also mixed noise and real-world noise.