论文标题
具有多路注意的多头卷积神经网络可改善图像
A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising
论文作者
论文摘要
最近，卷积神经网络（CNN）和注意机制已被广泛用于图像降级和达到令人满意的性能。但是，以前的作品主要使用一个头来接收嘈杂的图像，从而限制了提取功能的丰富性。因此，本文提出了一个具有多个头部（MH）的新型CNN（MH），其头将接收由不同旋转角旋转的输入图像。 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，我们提出了一种新型的多路注意机制（MPA）。与以前处理像素级，通道级或补丁级功能的注意机制不同，MPA专注于图像级别的功能。实验表明，MHCNN超过了其他最先进的CNN模型，上面有白色高斯噪声（AWGN）denoising和现实世界图像降解。它的峰值信噪比（PSNR）的结果高于其他网络，例如BRDNET，RIDNET，PAN-NET和CSANN。该代码可在https://github.com/jiahongz/mhcnn上访问。
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.
