In this paper, an adaptive contrast enhancement method based on the neighborhood conditional histogram is proposed to improve the visual quality of thermal infrared images. Existing block-based local contrast enhancement methods usually suffer from the over-enhancement of smooth regions or the loss of some details. To address these drawbacks, we first introduce a neighborhood conditional histogram to adaptively enhance the contrast and avoid the over-enhancement caused by the original histogram. Then the clip-redistributed histogram of the contrast-limited adaptive histogram equalization (CLAHE) is replaced by the neighborhood conditional histogram. In addition, the local mapping function of each sub-block is updated based on the global mapping function to further eliminate the block artifacts. Lastly, the optimized local contrast enhancement process, which combines both global and local enhanced results is employed to obtain the desired enhanced result. Experiments are conducted to evaluate the performance of the proposed method and the other five methods are introduced as a comparison. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the other block-based methods on local contrast enhancement, visual quality improvement, and noise suppression.

In this paper, an optimized contrast enhancement method combining global and local enhancement results is proposed to improve the visual quality of infrared images. Global and local contrast enhancement methods have their merits and demerits, respectively. The proposed method utilizes the complementary characteristics of these two methods to achieve noticeable contrast enhancement without artifacts. In our proposed method, the 2D histogram, which contains both global and local gray level distribution characteristics of the original image, is computed first. Then, based on the 2D histogram, the global and local enhanced results are obtained by applying histogram specification globally and locally. Lastly, the enhanced result is computed by solving an optimization equation subjected to global and local constraints. The pixel-wise regularization parameters for the optimization equation are adaptively determined based on the edge information of the original image. Thus, the proposed method is able to enhance the local contrast while preserving the naturalness of the original image. Qualitative and quantitative evaluation results demonstrate that the proposed method outperforms the block-based methods for improving the visual quality of infrared images.

For the uncooled long-wave infrared (LWIR) camera, the infrared (IR) irradiation the focal plane array (FPA) receives is a crucial factor that affects the image quality. Ambient temperature fluctuation as well as system power consumption can result in changes of FPA temperature and radiation characteristics inside the IR camera; these will further degrade the imaging performance. In this paper, we present a novel shutterless non-uniformity correction method to compensate for non-uniformity derived from the variation of ambient temperature. Our method combines a calibration-based method and the properties of a scene-based method to obtain correction parameters at different ambient temperature conditions, so that the IR camera performance can be less influenced by ambient temperature fluctuation or system power consumption. The calibration process is carried out in a temperature chamber with slowly changing ambient temperature and a black body as uniform radiation source. Enough uniform images are captured and the gain coefficients are calculated during this period. Then in practical application, the offset parameters are calculated via the least squares method based on the gain coefficients, the captured uniform images and the actual scene. Thus we can get a corrected output through the gain coefficients and offset parameters. The performance of our proposed method is evaluated on realistic IR images and compared with two existing methods. The images we used in experiments are obtained by a 384 × 288 pixels uncooled LWIR camera. Results show that our proposed method can adaptively update correction parameters as the actual target scene changes and is more stable to temperature fluctuation than the other two methods.

An adaptive contrast enhancement method is proposed in this paper. The proposed method is based on the contrast limited adaptive histogram equalization (CLAHE) and the histogram modification framework. The predefined clip point for the clipped histogram of each block in original CLAHE may still result in excessive contrast enhancement in homogeneous regions, which gives the enhanced image an unnatural look and creates visual artifacts. By replacing the clipped histogram with a modified histogram, the proposed method achieves success in adaptively enhancing contrast in each block based on its content. In addition to this, a novel mapping function is introduced to further improve the enhanced result of histogram equalization (HE). Experiments are conducted with both visible images and infrared images to evaluate the performance of the proposed method. The results show that the proposed method gets better performance on contrast enhancement and visual quality of the enhanced results.

In recent years, frequent network attacks have highlighted the importance of efficient detection methods for ensuring cyberspace security. This paper presents a novel intrusion detection system consisting of a data preprocessing stage and a deep learning model for accurately identifying network attacks. We have proposed four deep neural network models, which are constructed using architectures such as Convolutional Neural Networks (CNN), Bi-directional Long Short-Term Memory (BiLSTM), Bidirectional Gate Recurrent Unit (BiGRU), and Attention mechanism. These models have been evaluated for their detection performance on the NSL-KDD dataset.To enhance the compatibility between the data and the models, we apply various preprocessing techniques and employ the particle swarm optimization algorithm to perform feature selection on the NSL-KDD dataset, resulting in an optimized feature subset. Moreover, we address class imbalance in the dataset using focal loss. Finally, we employ the BO-TPE algorithm to optimize the hyperparameters of the four models, maximizing their detection performance. The test results demonstrate that the proposed model is capable of extracting the spatiotemporal features of network traffic data effectively. In binary and multiclass experiments, it achieved accuracy rates of 0.999158 and 0.999091, respectively, surpassing other state-of-the-art methods.

Due to the fast speed and high efficiency, discriminant correlation filter (DCF) has drawn great attention in online object tracking recently. However, with the improvement of performance, the costs are the increase in parameters and the decline of speed. In this paper, we propose a novel visual tracking algorithm, namely VDCFNet, and combine DCF with a vector convolutional network (VCNN). We replace one traditional convolutional filter with two novel vector convolutional filters in the convolutional stage of our network. This enables our model with few memories (only 59 KB) trained offline to learn the generic image features. In the online tracking stage, we propose a coarse-to-fine search strategy to solve drift problems under fast motion. Besides, we update model selectively to speed up and increase robustness. The experiments on OTB benchmarks demonstrate that our proposed VDCFNet can achieve a competitive performance while running over real-time speed.