In the field of computer vision, super-resolution reconstruction techniques based on deep learning have undergone considerable advancement; however, certain limitations remain, such as insufficient feature extraction and blurred image generation. To address these problems, we propose an image super-resolution reconstruction model based on a generative adversarial network. First, we employ a dual network structure in the generator network to solve the problem of insufficient feature extraction. The dual network structure is divided into an upsample subnetwork and a refinement subnetwork, which upsample and optimize a low-resolution image, respectively. In a scene with large upscaling factors, this structure can reduce the negative effect of noise and enhance the utilization of high-frequency details, thereby generating high-quality reconstruction results. Second, to generate sharper super-resolution images, we use the perceptual loss, which exhibits a fast convergence and excellent visual effect, to guide the generator network training. We apply the ResNeXt-50-32x4d network, which has few parameters and a large depth, to calculate the loss to obtain a reconstructed super-resolution image that is highly realistic. Finally, we introduce the Wasserstein distance into the discriminator network to enhance the discrimination ability and stability of the model. Specifically, this distance is employed to eliminate the activation function in the last layer of the network and avoid the use of the logarithm in calculating the loss function. Extensive experiments on the DIV2K, Set5, Set14, and BSD100 datasets demonstrate the effectiveness of the proposed model.

Aiming at the problem of spatial information loss in the semantic segmentation process, we propose a semantic segmentation network, termed the ventral and dorsal network (VDNet), which simulates the ventral and dorsal pathways of the cerebral visual cortex. The ventral pathway network focuses on extracting semantic information, and the dorsal pathway network focuses on extracting spatial information. We use the semantic enhancement module (SEM) in the ventral pathway network to fuse information of different scales to enhance the extraction of semantic information, and we use the spatial attention module (SAM) in the dorsal pathway network to assign weights to different locations in space to enhance the extraction of spatial information. By fusing the information of the two pathways, the final semantic segmentation result is obtained. Since the dorsal pathway network is used to specifically enhance the extraction of spatial information, the problem of spatial information loss during the segmentation process is effectively improved, and higher segmentation accuracy can be achieved by using only a small backbone network. On the CamVid, Cityscapes and PASCAL VOC 2012 datasets, we achieve the mean intersection over union (mIoU) of 82.1%, 77.8%, and 81.0%, respectively, which verifies the effectiveness of the proposed method.

A new method for load forecasting based on LS-SVM, PSO and wavelet transform is proposed. The wavelet transform is adopted to decompose the historical data, so the approximate part and several detail parts are obtained. The results of wavelet transform are predicted by a separate LS-SVM predictor. PSO is employed to determine these parameters of SVM model. The novel forecast model integrates the advantage of WT, PSO and LS-SVM. Compared with other predictors, this forecast model has greater generality ability and higher accuracy.