Single Image Super-Resolution Reconstruction Algorithm with Generative Adversarial Network Based on Multi-Size Convolution and Laplacian Filtering

At present, the image super-resolution reconstruction algorithm using deep learning still faces problems such as insufficient texture perception and insufficient realism of the reconstructed image. In order to improve the quality of the reconstructed image, a single image super-resolution reconstruction algorithm with generative ad-versarial network based on multi-size convolution and Laplacian filtering is proposed. First, multi-size convolu-tion feature extraction, separable Laplacian filtering and composite residual dense block are used to build a generation network which can extract more comprehensive image information. Secondly, multi-dimensional soft labels are utilized to construct the adversarial network, which can train the generative adversarial network easily and reconstruct image texture richly. Finally, the L1 loss function and the VGG low-level features are taken to obtain the overall features in the pre-training stage and the VGG high-level features, Charbonnier loss, and generative loss are used to make the reconstruction result more meticulously during training. Div2k and Flickr2K are chosen for model training, and Set5 and other data sets are used for testing. The experiment results show that the network size of this algorithm is 40% less than USRNet and other related algorithms, and the per-ceptual index is 0.76% lower than USRNet on average. The reconstruction result has more details and is more authentic. © 2021, Beijing China Science Journal Publishing Co. Ltd. All right reserved.