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Poor weather conditions, such as fog, haze, and mist, cause visibility degradation in captured images. Existing imaging devices lack the ability to effectively and efficiently mitigate the visibility degradation caused by poor weather conditions in real time. Image depth information is used to eliminate hazy effects by using existing physical model-based approaches. However, the imprecise depth information always affects dehazing performance. This article proposes an image fusion-based algorithm to enhance the performance and robustness of image dehazing. Based on a set of gamma-corrected underexposed images, pixelwise weight maps are constructed by analyzing both global and local exposedness to guide the fusion process. The spatial-dependence of luminance of the fused image is reduced, and its color saturation is balanced in the dehazing process. The performance of the proposed solution is confirmed in both theoretical analysis and comparative experiments.

Remote sensing images are widely used in object detection and tracking, military security, and other computer vision tasks. However, remote sensing images are often degraded by suspended aerosol in the air, especially under poor weather conditions, such as fog, haze, and mist. The quality of remote sensing images directly affect the normal operations of computer vision systems. As such, haze removal is a crucial and indispensable pre-processing step in remote sensing image processing. Additionally, most of the existing image dehazing methods are not applicable to all scenes, so the corresponding dehazed images may have varying degrees of color distortion. This paper proposes a novel atmospheric light estimation based dehazing algorithm to obtain high visual-quality remote sensing images. First, a differentiable function is used to train the parameters of a linear scene depth model for the scene depth map generation of remote sensing images. Second, the atmospheric light of each hazy remote sensing image is estimated by the corresponding scene depth map. Then, the corresponding transmission map is estimated on the basis of the estimated atmospheric light by a haze-lines model. Finally, according to the estimated atmospheric light and transmission map, an atmospheric scattering model is applied to remove haze from remote sensing images. The colors of the images dehazed by the proposed method are in line with the perception of human eyes in different scenes. A dataset with 100 remote sensing images from hazy scenes was built for testing. The performance of the proposed image dehazing method is confirmed by theoretical analysis and comparative experiments.

Person re-identification (Re-ID) is challenging due to host of factors: the variety of human positions, difficulties in aligning bounding boxes, and complex backgrounds, among other factors. This paper proposes a new framework called EXAM (EXtreme And Moderate feature embeddings) for Re-ID tasks. This is done using discriminative feature learning, requiring attention-based guidance during training. Here &ldquo, Extreme&rdquo, refers to salient human features and &ldquo, Moderate&rdquo, refers to common human features. In this framework, these types of embeddings are calculated by global max-pooling and average-pooling operations respectively, and then, jointly supervised by multiple triplet and cross-entropy loss functions. The processes of deducing attention from learned embeddings and discriminative feature learning are incorporated, and benefit from each other in this end-to-end framework. From the comparative experiments and ablation studies, it is shown that the proposed EXAM is effective, and its learned feature representation reaches state-of-the-art performance.

Due to the complex structure of a distributed energy resources system (DES) and a large amount of sensor data, local computers cannot provide enough computing resources to process the related data in a short time. Moreover, network integration causes a power system vulnerable to denial of service (DoS) attacks. DoS attacks result in the loss of partial sensor data, which affects the control performance of local computers on a power system. Therefore, this paper proposes a power system structure optimization strategy based on both sparse constraint optimization and cloud computing to solve the lack of computing power from local computers and prevent DoS attacks. Cloud computing is introduced to provide powerful computing resources for processing the related data in the proposed solution. The blocking probability of sensor data caused by DoS attacks is reduced by optimizing the sensor layout of a power system and reducing the transmission of sensor data. This paper also proposes a control strategy based on actor–critic reinforcement learning (RL) to maintain the stability of a power system during the structure optimization process. Three IEEE bus test systems are used to verify the effectiveness of the proposed structure optimization method and control strategy. The experimental results confirm that the proposed structure optimization method and control strategy can maintain the stability of a power system under DoS attacks.

Near-infrared (NIR) spectroscopy techniques have been widely used to classify tobacco cultivation regions. NIR spectroscopy of tobacco leaves involves a large number of correlated features, so it is difficult to find the connection between spectral data and tobacco cultivation regions. This paper proposes a novel classification model of tobacco cultivation regions that integrates residual network (ResNet) and NIR spectroscopy techniques. As the number of neural network layers increases, the network may have issues such as network degradation, gradient disappearance, and the reduction of sample recognition rate. The proposed model applies a residual module to a neural network which effectively solves or alleviates the vanishing gradient issues caused by the increase of network depth. This paper also adds balance and suppression factors to the loss function to solve the issues caused by uneven sizes of tobacco samples collected from different cultivation regions in the training process. In the proposed method, tobacco samples are marked as internal and external samples respectively during the training process. Internal samples are collected from the corresponding cultivation regions in the north, northeast, and northwest of Guizhou Province, China. External samples are collected from other cultivation regions. The weight distributions of internal and external samples can be adjusted by experimental results to improve the identification accuracy of the proposed solution. The size of training samples determines the generalization ability of the network and affects the experimental results. A parametric rectified linear unit (PReLU) function is integrated into the network, in which the parameters of a linear unit are adaptively learned to further improve the identification accuracy of the proposed solution. Compared with current mainstream methods, the experimental results confirm that the proposed model is superior in accurately identifying different cultivation regions of tobacco leaves.