Dual-branch Feature Fusion Network for image denoising

The goal of image denoising is to reduce or eliminate noise in images, thereby restoring and enhancing the true details and quality of the images. This process is essential for improving the accuracy of image analysis, enhancing image recognition, and optimizing visual effects. Despite the widespread application and notable success of deep convolutional neural networks (CNNs) in image denoising tasks, existing methods often encounter challenges such as overfitting and limited flexibility. To address these issues, this paper proposes a Dual-Branch Denoising Network (DFFNet) designed to enhance feature extraction capabilities and improve adaptability to noise. Specifically, the proposed DFFNet comprises two distinct parallel branches. In the first branch, a novel Multi-Scale Convolutional Block (MCB) is introduced, which significantly increases the receptive field while simultaneously preserving local details and the overall structure of the image. In the second branch, a Flexible Pooling Attention mechanism (FCA) is designed to reduce computational complexity while enhancing the effectiveness of feature representation. The two branches are then weighted and fused, achieving the dual objectives of noise removal and the preservation of critical image details, thereby improving overall image quality. Extensive experiments on multiple datasets demonstrate that the proposed DFFNet achieves superior denoising performance.