Multi-branch network for brain image denoising based on U-shaped dynamic convolution and multi-scale feature extraction

Brain images are often disturbed by noise during acquisition, compromising subsequent medical analysis and reducing diagnostic accuracy. Effective denoising that preserves the structural details is therefore essential. This essay proposes a multi-branch convolutional neural network for brain image denoising, integrating a U-shaped network, dynamic convolution, and multi-scale feature extraction. The model includes an attention-enhanced encoder–decoder to improve feature representation, dynamic convolution with a sparse mechanism for adaptive global modeling, multi-scale dense residual blocks with depth-separable convolution to capture local details efficiently, and a multi-branch fusion strategy to process features at different scales in parallel and refine them. Experiments on brain image datasets demonstrate that the proposed method achieves superior denoising performance, effectively suppressing noise while retaining fine structural details. In conclusion, the network significantly enhances the quality of brain images and shows potential for improving the accuracy of subsequent medical image analysis and diagnosis.