MACFNet: multi-attention complementary fusion network for image denoising

Recent years, thanks to the prosperous development of deep convolutional neural network, image denoising task has achieved unprecedented achievements. However, previous researches have difficulties in keeping the balance between noise removing and textual details preserving, even bringing the negative effect, such as local blurring. To overcome these weaknesses, in this paper, we propose an innovative multi-attention complementary fusion network (MACFNet) to restore delicate texture details while eliminating noise to the greatest extent. To be specific, our proposed MACFNet mainly composes of several multi-attention complementary fusion modules (MACFMs). Firstly, we use feature extraction block (FEB) to extract basic features.Then, we use spatial attention (SA), channel attention (CA) and patch attention (PA) three different kinds of attention mechanisms to extract spatial-dimensional, channel-dimensional and patch-dimensional attention aware features, respectively. In addition, we attempt to integrate three attention mechanisms in an effective way. Instead of directly concatenate, we design a subtle complementary fusion block (CFB), which is skilled in incorporating three sub-branches characteristics adaptively. Extensive experiments are carried out on gray-scale image denoising, color image denoising and real noisy image denoising. The quantitative results (PSNR) and visual effects all prove that our proposed network achieves great performance over some state-of-the-art methods.