基于多通道注意力机制的图像超分辨率重建网络

针对现有的图像超分辨率重建方法存在生成图像纹理扭曲、细节模糊等问题，提出了一种基于多通道注意力机制的图像超分辨率重建网络。首先，该网络中的纹理提取模块通过设计多通道注意力机制并结合一维卷积实现跨通道的信息交互，以关注重要特征信息；然后，该网络中的纹理恢复模块引入密集残差块来尽可能恢复部分高频纹理细节，从而提升模型性能并产生优质重建图像。所提网络不仅能够有效提升图像的视觉效果，而且在基准数据集CUFED5上的结果表明所提网络与经典的基于卷积神经网络的超分辨率重建（SRCNN）方法相比，峰值信噪比（PSNR）和结构相似度（SSIM）分别提升了1.76 dB和0.062。实验结果表明，所提网络可提高纹理迁移的准确性，并有效提升生成图像的质量。

Image super-resolution reconstruction network based on multi-channel attention mechanism

The existing image super-resolution reconstruction methods are affected by texture distortion and details blurring of generated images. To address these problems， a new image super-resolution reconstruction network based on multi-channel attention mechanism was proposed. Firstly， in the texture extraction module of the proposed network， a multi-channel attention mechanism was designed to realize the cross-channel information interaction by combining one-dimensional convolution， thereby achieving the purpose of paying attention to important feature information. Then， in the texture recovery module of the proposed network， the dense residual blocks were introduced to recover part of high-frequency texture details as many as possible to improve the performance of model and generate high-quality reconstructed images. The proposed network is able to improve visual effects of reconstructed images effectively. Besides， the results on benchmark dataset CUFED5 show that the proposed network has achieved the 1.76 dB and 0.062 higher in Peak Signal-to-Noise Ratio （PSNR） and Structural SIMilarity （SSIM） compared with the classic Super-Resolution using Convolutional Neural Network （SRCNN） method. Experimental results show that the proposed network can increase the accuracy of texture migration， and effectively improve the quality of generated images.