A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising

Recently, convolutional neural networks (CNNs) and attention mechanisms have been widely used in image denoising and achieved satisfactory performance. However, the previous works mostly use a single head to receive the noisy image, limiting the richness of extracted features. Therefore, a novel CNN with multiple heads (MH) named MHCNN is proposed in this paper, whose heads will receive the input images rotated by different rotation angles. MH makes MHCNN simultaneously utilize features of rotated images to remove noise. We also present a novel multi-path attention mechanism (MPA) to integrate these features effectively. Unlike previous attention mechanisms that handle pixel-level, channel-level, and patch-level features, MPA focuses on features at the image level. Experiments show MHCNN surpasses other state-of-the-art CNN models on additive white Gaussian noise (AWGN) denoising and real-world image denoising. Its peak signal-to-noise ratio (PSNR) results are higher than other networks, such as DnCNN, BRDNet, RIDNet, PAN-Net, and CSANN. It is also demonstrated that the proposed MH with MPA mechanism can be used as a pluggable component.