FEMRNet: Feature-enhanced multi-scale residual network for image denoising

Deep convolutional neural networks (DCNN) have attracted considerable interest in image denoising because of their excellent learning capacity. However, most of the existing methods cannot fully extract and utilize the fine features during denoising, resulting in insufficient detailed information extracted and limited model expression ability, especially in complex denoising tasks. Inspired by the above challenges, in this paper, a feature-enhanced multi-scale residual network (FEMRNet) is proposed, mainly including an enhanced feature extraction block (EFEB), a multi-scale residual backbone (MSRB), a detail information recovery block (DIRB) and a merge reconstruction block (MRB). Specifically, the EFEB can increase the receptive field through dilated convolution with different expansion factors, and multi-scale convolution can further enhance the feature. The MSRB integrates global and local feature information through residual denoising blocks and skip connections to enhance the inferencing ability of denoising models. The DIRB is used to finely extract the information in the image, and combine the timing information by convGRU to restore the image details. Finally, MRB is designed to construct a clean image by subtracting the fused noise mapping obtained from MSRB and DIRB with a given noisy image. Additionally, extensive experiments are implemented on commonly-used denoising benchmarks. Comparison experiments with state-of-the-art methods and ablation experiments show that our method achieves promising performance in denoising tasks.