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.

翻译：最近,连动神经网络(CNNs)和关注机制被广泛用于图像脱色和取得令人满意的性能,然而,前几部作品大多使用一个头来接收噪音图像,限制了提取功能的丰富性。因此,本文中提议了一部名为MHN(MH)的新型CNN,其头部将接收由不同旋转角度旋转的输入图像。MH使MHCNN同时利用旋转图像的特征来消除噪音。为了有效地整合这些特征,我们提出了一个新的多路关注机制。与以往处理像素级、频道级或补接层特征的注意机制不同,MPA侧重于图像级的特征。实验显示MHNNN超越了其他关于添加白高音噪音(AWGN)的现代化CN模式。其顶峰信号到噪音比率(PSNR)高于其他网络,如BRDNet、RIDNet、PAN-Net和CSANMM。该代码可在 http://NMS/NHG.MHA/NG.