Images taken in hazy weather are susceptible to the effects of haze, resulting in blurred images and low contrast to the extent that important information is lost in the image. Therefore, this is necessary to dehaze haze images, process the image information and ensure the normal operation of other computer vision tasks. Traditional deep learning-based image dehazing methods often suffer from uneven haze removal, colour bias and loss of detail. To solve this problem, this paper proposes a single image dehazing method (IMNet) based on pyramidal input for image dehazing. The network is divided into three modules: an intensive feature extraction module, a pyramid input branch and a detail deepening module. This paper uses two loss functions in combination, which can help preserve texture details more effectively. Experimental results have shown that IMNet outperforms other dehazing algorithms in terms of metrics and visual effects.
Obtaining change information in different periods from a pair of registered satellite remote sensing images is of great significance to urban planning, so change detection (CD) technology has attracted extensive attention in recent years. In recent years, convolutional neural networks have set off a boom in many artificial intelligence research fields because of their excellent feature extraction performance. However, the common convolution operation mainly focuses on the abstraction of the semantic information of the features, which often leads to the details of the features being ignored and thus affects the final accuracy. For example, the contour details of changing objects and the structural information of small objects are often lost. We propose a Siamese network that enhances contour and structural details to achieve higher-accuracy CD tasks for bitemporal remote sensing images. In this network, we propose an efficient contour-enhanced convolutional block that is based on the reparameterization technique. The contour-enhanced convolutional block strengthens the extraction of structural and contour features by integrating different branches. In addition, inspired by NestedUNet and to better preserve the original location information of features, we use a dense connection as the feature extractor to obtain refined features of bitemporal images. After that, we use a difference module to calculate the change characteristics of the dual-time image, and we use atrous spatial pyramid pooling and enhanced spatial attention to further refine the obtained change characteristics. We conduct extensive experiments on three different datasets to verify the effectiveness of our model. Experimental results show that our method outperforms state-of-the-art methods in both overall accuracy and visualization details.
Change detection (CD) is the operation of quantitatively analyzing the surface changes of a phenomenon or objects over two different times. Lately, CD based on deep learning has developed to become more and more powerful, and convolutional neural networks (CNNs) have dominated the field of remote sensing (RS) CD. In particular, in many fields of computer vision, neural networks based on U-Net network and skip connections have been generally used. However, despite the excellent performance achieved by CNN, it does not learn global and long-range semantic information interaction well due to the locality of convolutional operations. The recently proposed Swin-UNet in the field of medical image segmentation achieved excellent results, which is a U-Net-like pure transformer. In the face of the challenge of segmentation accuracy, the Swin transformer has demonstrated strong capabilities. The Swin transformer block (STB) consists of residual connected STBs used in SwinIR to enhanced training stability. We began to try to incorporate them into our network for RS CD. Finally, we propose a transformer-based multi-scale feature fusion model (TMFF), including decoder, encoder, and skip connection structure, for RS image CD. We modify the original U-Net architecture so that it can better aggregate semantic features at all levels. Our proposed TMFF achieves impressive results through experiments on three datasets;
Most existing multispectral fusion algorithms often suffer from spectral or spatial information distortion. Driven by this motivation, we propose an edge-guided multispectral (MS) image fusion algorithm. In particular, it combines the advantages of generative adversarial networks and improved fusion frameworks, so the merged image can better preserve the spectral information of the original multispectral image while injecting spatial detail information. Specifically, first, an MS image with more image detail is generated using the generated confrontation network for preliminary reconstruction. The panchromatic image edge information and the antagonistic learning strategy are introduced for the robust multispectral image reconstruction. Then, using the reconstructed MS image and the general component substitution image fusion framework, the whole fusion system of this paper is constructed. An enhancement operator is introduced to inject spatial details. Our extensive dataset evaluations show that our approach performs better in terms of high objective quality and human visual perception than several of the most advanced fusion methods.
In order to improve the accuracy of short-term load forecasting of power system, the paper proposes a power load forecasting model RVR based on particle swarm optimization (PSO), and compares it with the support vector regression model. Aiming at the randomness of the parameters of the correlation vector regression, that is, the penalty function and the kernel function in the initialization, the PSO algorithm is used to optimize the parameters of the correlation vector regression, which can achieve better prediction results. The classical particle swarm optimization algorithm is a global optimization algorithm that can quickly find the optimal parameters in the correlation vector regression. The RVR model based on the particle swarm optimization is applied to short-term load forecasting. The simulation results show that the convergence rate of the optimized model of particle swarm optimization is more accurate than that of the traditional prediction models of SVR and RVR, and the predicting accuracy of the PSO – RVR model is higher than that of the PSO - SVR, which verifies the feasibility of the correlation vector regression method based on particle swarm optimization algorithm in the short-term load forecasting, which has practical value to some degree.
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