关键词: 图像处理, 超分辨率重建, 卷积神经网络, 多尺度特征提取, 分而治之

Abstract: In view of the problems in the existing convolutional neural network-based super-resolution network, such as difficult convergence of training, unable to adapt to multiple upsampling coefficients and unable to sample non-integer upsampling coefficients, an image super-resolution reconstruction algorithm of adaptive upsampling module based on multi-scale and the idea of divide-and-conquer is proposed in this paper. In the algorithm, the improved multi-scale channel attention feature extraction module is used to extract multi-scale features of low-resolution images to generate feature maps at different scales, and then the global feature fusion is realized by inputting them into the bottleneck layer. The super-resolution images are obtained by using the adaptive upsampling module based on divide-and-conquer to solve the adaptation problem of different upsampling coefficients and the upsampling problem of non-integer upsampling coefficients. In the contrast experiment, the proposed algorithm still has good convergence without any initialization methods. Under the integer magnification factor, the image reconstruction performance of the proposed algorithm exceeds the current mainstream super-resolution network, and the PSNR and SSIM performance are improved by 0.34 dB and 0.0391 respectively compared with MRFN. Under the non-integer magnification factor, the average PSNR performance is improved by 1.24 dB compared with the bicubic interpolation method, and there is not necessary to train each magnification factor.

Key words: image processing, super-resolution reconstruction, convolutional neural network, multi-scale feature extraction, divide-and-conquer