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Due to the impact of high dust and low illumination in underground environments, mine images have problems such as low resolution and blurry details. When existing image super-resolution reconstruction algorithms are applied to mine images, it is difficult to obtain image information at different scales. The network parameters are too large, which affects the reconstruction speed. The reconstructed images are prone to problems such as detail loss, blurry edge contours, and artifacts. A mine image super-resolution reconstruction algorithm based on multi-scale dense channel attention super-resolution generative adversarial network (SRGAN) is proposed. A multi-scale dense channel attention residual block is designed to replace the original residual block of SRGAN. Two parallel dense connected blocks with different convolutional kernel sizes are used to fully obtain image features. The efficient channel attention modules are integrated to enhance attention to high-frequency information. The depthwise separable convolution is used to lighten the network and suppress the increase of network parameters. The texture loss constraint network training is utilized to avoid artifacts during network deepening. Experiments are conducted on the proposed mine image super-resolution reconstruction algorithm and classic super-resolution reconstruction algorithms BICUBIC, SRCNN, SRRESNET, SRGAN on both underground and public datasets. The results show that the proposed algorithm outperformed the comparative algorithm in both subjective and objective evaluations. Compared to SRGAN, the proposed algorithm reduces network parameters by 2.54%. Compared to the average index values of the classic algorithms, the peak signal-to-noise ratio and structural similarity of the proposed algorithm increase by 0.764 dB and 0.053 58 respectively. It can better focus on the texture, contour and other details of the image, and the reconstructed image is more in line with human vision.

During the acquisition of infrared images in substations, low-quality images with poor contrast, blurred details, and missing texture information frequently appear, which adversely affects subsequent advanced visual tasks. To address this issue, this paper proposes an infrared image enhancement algorithm for substation equipment based on a self-attention cycle generative adversarial network (SA-CycleGAN). The proposed algorithm incorporates a self-attention mechanism into the CycleGAN model's transcoding network to improve the mapping ability of infrared image information, enhance image contrast, and reducing the number of model parameters. The addition of an efficient local attention mechanism (EAL) and a feature pyramid structure within the encoding network enhances the generator's ability to extract features and texture information from small targets in infrared substation equipment images, effectively improving image details. In the discriminator part, the model's performance is further enhanced by constructing a two-channel feature network. To accelerate the model's convergence, the loss function of the original CycleGAN is optimized. Compared to several mainstream image enhancement algorithms, the proposed algorithm improves the quality of low-quality infrared images by an average of 10.91% in color degree, 18.89% in saturation, and 29.82% in feature similarity indices. Additionally, the number of parameters in the proposed algorithm is reduced by 37.89% compared to the original model. Finally, the effectiveness of the proposed method in improving recognition accuracy is validated by the Centernet target recognition algorithm. [ABSTRACT FROM AUTHOR]

Abstract: Using the material of Hemarthria compressa (L.F.) R.Br. cv. YA’AN, we carried out this research to study the influence of radiation on the genetic variation of plants. Genetic difference was analyzed with expressed sequence tag‐simple sequence repeat (EST‐SSR) molecular marker through the comparison of 60Co‐γ radiation on H. compressa seed stems and original variety. By using 20 primer pairs, 176 polymerase chain reaction (PCR)‐amplifications with clear and consistent bands were obtained. The results showed that 155 of 176 bands were polymorphic, which indicating an 88.07% polymorphism rate, and each pair of primers had 8.8 amplified bands on average; the amplitude of polymorphism information content was 0.4709–0.6952 with an average value 0.6081. The genetic similarity coefficient of H. compressa and its mutants ranged from 0.4318 to 0.8239 with an average of 0.6671. As a consequence, existence of genetic differences between the mutants and the basic material was proved. [ABSTRACT FROM AUTHOR]