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.
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,以往的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MHCN同时使用旋转图像的特征来消除噪音。我们还提出了一个新的多路关注机制(MPA)来有效整合这些特征。与以往处理像素级、频道级和补接层特征的注意机制不同,MPA侧重于图像层面的特征。实验显示MHNNN超越了其他最先进的CNN关于添加白高山噪音(AWGN)的模型。MNN(PSNR)的峰值信号到噪音比率(PSNR)高于其他网络,例如DCNNN、BRDNet、RDNet、RIDNet、PAN-Net、PAN-Net、CANHA(CAN)和CANMAN(MAN)的终端部分也显示MA的MA是MSA的MA的MA。