The proposal of perceptual loss solves the over-smoothing problem of images caused by pixel-wise loss and improves the visual quality of the images, but it also inevitably produces a large number of artifacts and distortions in images. The reason for this phenomenon is that the perceptual features only rely on a single pre-trained visual geometry group (VGG) network, which results in the features of the image being unable to be fully extracted, thus limiting the reasoning ability of the model. To fundamentally reduce the generation of artifacts and distortions, this paper proposes the Dual Perceptual Loss (DP Loss). First, we improve the perceptual feature extraction method so that it no longer only extracts single-type VGG features. In addition, a residual network (ResNet) feature that has a complementary relationship with the VGG feature can also be extracted. Then, we propose a dynamic weighting method to eliminate the magnitude difference between perceptual losses. Finally, to obtain the excellent effect of image reconstruction, enhanced super-resolution generative adversarial network (ESRGAN) with strong learning capability is used in this paper to adapt the complexity of DP Loss. The abundant experimental studies and evaluations are conducted on benchmark datasets. Results are encouraging and better than those previously reported on these datasets. The code is available at https://github.com/Sunny6-6-6/ESRGAN-DP.

In the field of super-resolution reconstruction, generative adversarial networks are able to generate textures that are more in line with the perception of the human eyes, but low-resolution images often encounter information loss and edge blurring problems in the process of reconstruction. In order to solve this problem, this article proposed an image super-resolution reconstruction model based on an enhanced attention mechanism and gradient loss, which can better focus on important details in low-resolution images, thus improving the quality of reconstructed images. Firstly, an enhanced attention mechanism is proposed and incorporated into the generator model as a way to reduce the amount of information loss during image feature extraction and retain more image details. Furthermore, this paper proposes a gradient correlation loss function that aims to maximize the correlation between the gradient of the generated image and the gradient of the original image. Thus, the generated image is more realistic and maintains a consistent edge structure. Finally, the experimental results on the standard dataset show that compared with other representative algorithms, the proposed model has achieved some improvement in PSNR, SSIM, and LPIPS, which can verify the effectiveness of the algorithm.

Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.