Underwater image enhancement has received much attention in underwater vision research. However, raw underwater images easily suffer from color distortion, underexposure, and fuzz caused by the underwater scene. To address the above-mentioned problems, we propose a new multiscale dense generative adversarial network (GAN) for enhancing underwater images. The residual multiscale dense block is presented in the generator, where the multiscale, dense concatenation, and residual learning can boost the performance, render more details, and utilize previous features, respectively. And the discriminator employs computationally light spectral normalization to stabilize the training of the discriminator. Meanwhile, nonsaturating GAN loss function combining $L_1$ loss and gradient loss is presented to focus on image features of ground truth. Final enhanced results on synthetic and real underwater images demonstrate the superiority of the proposed method, which outperforms nondeep and deep learning methods in both qualitative and quantitative evaluations. Furthermore, we perform an ablation study to show the contributions of each component and carry out application tests to further demonstrate the effectiveness of the proposed method.

Pan-sharpening aims at acquiring a multi-spectral image with high spatial resolution by fusing a low resolution multi-spectral image and a panchromatic image. In order to improve spatial details and reduce spectral distortions, we develop a new pan-sharpening model based on the Bayesian theory, which involves three assumptions: (1) The low resolution multi-spectral images are generally decimated from the high resolution multi-spectral images by convolution with a blurring kernel. (2) Different from most pan-sharpening methods that use linear manners to preserve spatial information, we attempt a non-linear manner based on a convolutional neural network composed of the proposed multi-scale recursive blocks. And we train our network parameters in multi-order gradient domains to preserve more spatial structures. (3) We introduce an anisotropic total variation (TV) prior in multi-order gradient domains to reconstruct better image edges and details. We establish the posterior probability model based on the above assumptions and derive an efficient optimization scheme to address the proposed objective function. Final experimental results demonstrate that the proposed model can overcome the restriction of linear model and achieve better spectral and spatial fusion, compared with several traditional and deep learning-based pan-sharpening approaches. In addition, our model achieves more promising generalization across different satellites than other deep learning-based methods.

In order to solve the problems of low accuracy and missed detection in traditional blood cell data detection tasks. This paper proposes and implements the blood cell detection method based on the YOLOv5 (YOLOv5-ALT). The goal of this research is to enhance the accuracy of the detection with the YOLO techniques. This work presents the method overcomes the shortcomings of the existing method by introducing the attention mechanism in the feature channel, modifying SPP module in YOLOv5 backbone feature extraction network, and changing the bounding box regression loss function. Based on the deep learning object detection algorithm, each evaluation index is compared to evaluate the effectiveness of the model. Experimental results show that the mAP@0.5, Precision and Recall of the YOLOv5-ALT reaches 97.4%, 97.9% and 93.5%. This method is more in line with the effectiveness of the blood cell detection task.

The steady-state electrical conduction current for single and multilayer polyimide (PI) nanocomposite films was observed at the low and high electric field for different temperatures. Experimental data were fitted to conduction models to investigate the dominant conduction mechanism in these films. In most films, space charge limited current (SCLC) and Poole–Frenkel current displayed dominant conduction. At a high electric field, the ohmic conduction was replaced by current–voltage dependency. Higher conduction current was observed for nanocomposite films at a lower temperature, but it declined at a higher temperature. PI nanocomposite multilayer films showed a huge reduction in the conduction current at higher electric fields and temperatures. The conclusions derived in this study would provide the empirical basis and early breakdown phenomenon explanation when performing dielectric strength and partial discharge measurements of PI-based nanocomposite insulation systems of electric motors.