  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. To integrate
these features effectively, we present a novel multi-path attention mechanism
(MPA). Unlike previous attention mechanisms that handle pixel-level,
channel-level, or 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 BRDNet, RIDNet, PAN-Net, and CSANN. The code is accessible at
https://github.com/JiaHongZ/MHCNN.
