Wang Shanqin, Zhang Miao, Miao Mengjun
School of Information Engineering, Chuzhou Polytechnic, Chuzhou, China.
School of Computer, Qinghai Normal University, Xining, China.
Front Neurorobot. 2024 Aug 16;18:1436052. doi: 10.3389/fnbot.2024.1436052. eCollection 2024.
Aiming at the problems of traditional image super-resolution reconstruction algorithms in the image reconstruction process, such as small receptive field, insufficient multi-scale feature extraction, and easy loss of image feature information, a super-resolution reconstruction algorithm of multi-scale dilated convolution network based on dilated convolution is proposed in this paper. First, the algorithm extracts features from the same input image through the dilated convolution kernels of different receptive fields to obtain feature maps with different scales; then, through the residual attention dense block, further obtain the features of the original low resolution images, local residual connections are added to fuse multi-scale feature information between multiple channels, and residual nested networks and jump connections are used at the same time to speed up deep network convergence and avoid network degradation problems. Finally, deep network extraction features, and it is fused with input features to increase the nonlinear expression ability of the network to enhance the super-resolution reconstruction effect. Experimental results show that compared with Bicubic, SRCNN, ESPCN, VDSR, DRCN, LapSRN, MemNet, and DSRNet algorithms on the Set5, Set14, BSDS100, and Urban100 test sets, the proposed algorithm has improved peak signal-to-noise ratio and structural similarity, and reconstructed images. The visual effect is better.
针对传统图像超分辨率重建算法在图像重建过程中存在的感受野小、多尺度特征提取不足以及图像特征信息易丢失等问题，本文提出了一种基于空洞卷积的多尺度空洞卷积网络超分辨率重建算法。首先，该算法通过不同感受野的空洞卷积核从同一输入图像中提取特征，得到不同尺度的特征图；然后，通过残差注意力密集块，进一步获取原始低分辨率图像的特征，添加局部残差连接以融合多通道之间的多尺度特征信息，同时使用残差嵌套网络和跳跃连接来加速深度网络收敛并避免网络退化问题。最后，深度网络提取特征，并将其与输入特征融合以增加网络的非线性表达能力，从而增强超分辨率重建效果。实验结果表明，在Set5、Set14、BSDS100和Urban100测试集上，与双立方（Bicubic）、超分辨率卷积神经网络（SRCNN）、高效子像素卷积神经网络（ESPCN）、非常深的超分辨率网络（VDSR）、深度递归卷积网络（DRCN）、拉普拉斯金字塔超分辨率网络（LapSRN）、记忆网络（MemNet）和深度超分辨率残差网络（DSRNet）算法相比，该算法提高了峰值信噪比和结构相似性，重建图像的视觉效果更好。
