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Traditional deep-learning-based fusion algorithms usually take the original image as input to extract features, which easily leads to a lack of rich details and background information in the fusion results. To address this issue, we propose a fusion algorithm, based on mutually guided image filtering and cross-transmission, termed MGFCTFuse. First, an image decomposition method based on mutually guided image filtering is designed, one which decomposes the original image into a base layer and a detail layer. Second, in order to preserve as much background and detail as possible during feature extraction, the base layer is concatenated with the corresponding original image to extract deeper features. Moreover, in order to enhance the texture details in the fusion results, the information in the visible and infrared detail layers is fused, and an enhancement module is constructed to enhance the texture detail contrast. Finally, in order to enhance the communication between different features, a decoding network based on cross-transmission is designed within feature reconstruction, which further improves the quality of image fusion. In order to verify the advantages of the proposed algorithm, experiments are conducted on the TNO, MSRS, and RoadScene image fusion datasets, and the results demonstrate that the algorithm outperforms nine comparative algorithms in both subjective and objective aspects. [ABSTRACT FROM AUTHOR]

Coal petrography extraction is crucial for the accurate analysis of coal reaction characteristics in coal gasification, coal coking, and metal smelting. Nevertheless, automatic extraction remains a challenging task because of the grayscale overlap between exinite and background regions in coal photomicrographs. Inspired by the excellent performance of neural networks in the image segmentation field, this study proposes a reliable coal petrography extraction method that achieves precise segmentation of coal petrography from the background regions. This method uses a novel semantic segmentation model based on Unet, referred to as M2AR-Unet. To improve the efficiency of network learning, the proposed M2AR-Unet framework takes Unet as a baseline and further optimizes the network structure in four ways, namely, an improved residual block composed of four units, a mixed attention module containing multiple attention mechanisms, an edge feature enhancement strategy, and a multiscale feature extraction module composed of a feature pyramid and atrous spatial pyramid pooling module. Compared to current state-of-the-art segmentation network models, the proposed M2AR-Unet offers improved coal petrography extraction integrity and edge extraction. [ABSTRACT FROM AUTHOR]

This study presents a prediction model for high-power electromagnetic pulse (HPEMP) effects on aboveground vehicles based on convolutional neural networks (CNNs). Since a vehicle is often located aboveground and is close to the air-ground–half-space interface, the electromagnetic energy coupled into the vehicle by the ground reflected waves cannot be ignored. Consequently, the analysis of the vehicle's HPEMP effect is a composite electromagnetic scattering problem of the half-space and the vehicles above it, which is often analyzed using different half-space numerical methods. However, traditional numerical methods are often limited by the complexity of the actual half-space models and the high computational demands of complex targets. In this study, a prediction method is proposed based on a CNN, which can analyze the electric field and energy density under different incident conditions and half-space environments. Compared with the half-space finite-difference time-domain (FDTD) method, the accuracy of the prediction results was above 98% after completing the training of the CNN network, which proves the correctness and effectiveness of the method. In summary, the CNN prediction model in this study can provide a reference for evaluating the HPEMP effect on the target over a complex half-space medium. [ABSTRACT FROM AUTHOR]

The crater is one of the main obstacles that need to be avoided when Mars probe lands. In order to further improve the accuracy of crater detection, this paper proposes a combined detection method based on deep learning. Firstly, the random structured forest is trained offline to detect the edge information of craters. Secondly, according to the detected edge information of the crater, the candidate areas of the crater are determined with the morphological method. For the identified candidate areas of the crater, Alexnet network trained by transfer learning was used to identify crater areas. Compared with other methods, the proposed method has relatively good effect. [ABSTRACT FROM AUTHOR]

As the medium of human-computer interaction, it is crucial to correctly and quickly interpret the motion information of surface electromyography (sEMG). Deep learning can recognize a variety of sEMG actions by end-to-end training. However, most of the existing deep learning approaches have complex structures and numerous parameters, which make the network optimization problem difficult to realize. In this paper, a novel PSO-based optimized lightweight convolution neural network (PLCNN) is designed to improve the accuracy and optimize the model with applications in sEMG signal movement recognition. With the purpose of reducing the structural complexity of the deep neural network, the designed convolution neural network model is mainly composed of three convolution layers and two full connection layers. Meanwhile, the particle swarm optimization (PSO) is used to optimize hyperparameters and improve the autoadaptive ability of the designed sEMG pattern recognition model. To further indicate the potential application, three experiments are designed according to the progressive process of body movements with respect to the Ninapro standard data set. Experiment results demonstrate that the proposed PLCNN recognition method is superior to the four other popular classification methods. [ABSTRACT FROM AUTHOR]

To compare the image quality of low-dose CT urography (LD-CTU) using deep learning image reconstruction (DLIR) with conventional CTU (C-CTU) using adaptive statistical iterative reconstruction (ASIR-V). This was a prospective, single-institutional study using the excretory phase CTU images for analysis. Patients were assigned to the LD-DLIR group (100kV and automatic mA modulation for noise index (NI) of 23) and C-ASIR-V group (100kV and NI of 10) according to the scan protocols in the excretory phase. Two radiologists independently assessed the overall image quality, artifacts, noise and sharpness of urinary tracts. Additionally, the mean CT attenuation, signal-to-noise ratio (SNR) and contrast-to-noise (CNR) in the urinary tracts were evaluated. 26 patients each were included in the LD-DLIR group (10 males and 16 females; mean age: 57.23 years, range: 33–76 years) and C-ASIR-V group (14 males and 12 females; mean age: 60 years, range: 33–77 years). LD-DLIR group used a significantly lower effective radiation dose compared with the C-ASIR-V group (2.01 ± 0.44 mSv vs 6.9 ± 1.46 mSv, p < 0.001). LD-DLIR group showed good overall image quality with average score >4 and was similar to that of the C-ASIR-V group. Both groups had adequate and similar attenuation value, SNR and CNR in most segments of urinary tracts. It is feasibility to provide comparable image quality while reducing 71% radiation dose in low-dose CTU with a deep learning image reconstruction algorithm compared to the conventional CTU with ASIR-V. (1) CT urography with deep learning reconstruction algorithm can reduce the radiation dose by 71% while still maintaining image quality. [ABSTRACT FROM AUTHOR]

To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT. This was a prospective study. 40 patients with hepatic lesions underwent abdominal CT using routine dose (120kV, noise index (NI) setting of 11 with automatic tube current modulation) in the arterial-phase (AP) and portal-phase (PP), and low dose (NI = 24) in the delayed-phase (DP). All images were reconstructed at 1.25 mm thickness using ASIR-V at 50% strength. In addition, images in DP were reconstructed using DLIR in high setting (DLIR-H). The CT value and standard deviation (SD) of hepatic parenchyma, spleen, paraspinal muscle and lesion were measured. The overall image quality includes subjective noise, sharpness, artifacts and diagnostic confidence were assessed by two radiologists blindly using a 5-point scale (1, unacceptable and 5, excellent). Dose between AP and DP was compared, and image quality among different reconstructions were compared using SPSS20.0. Compared to AP, DP significantly reduced radiation dose by 76% (0.76 ± 0.09 mSv vs 3.18 ± 0.48 mSv), DLIR-H DP images had lower image noise (14.08 ± 2.89 HU vs 16.67 ± 3.74 HU, p < 0.001) but similar overall image quality score as the ASIR-V50% AP images (3.88 ± 0.34 vs 4.05 ± 0.44, p > 0.05). For the DP images, DLIR-H significantly reduced image noise in hepatic parenchyma, spleen, muscle and lesion to (14.77 ± 2.61 HU, 14.26 ± 2.67 HU, 14.08 ± 2.89 HU and 16.25 ± 4.42 HU) from (24.95 ± 4.32 HU, 25.42 ± 4.99 HU, 23.99 ± 5.26 HU and 27.01 ± 7.11) with ASIR-V50%, respectively (all p < 0.001) and improved image quality score (3.88 ± 0.34 vs 2.87 ± 0.53; p < 0.05). DLIR-H significantly reduces image noise and generates images with clinically acceptable quality and diagnostic confidence with 76% dose reduction. (1) DLIR-H yielded a significantly lower image noise, higher CNR and higher overall image quality score and diagnostic confidence than the ASIR-V50% under low signal conditions. (2) Our study demonstrated that at 76% lower radiation dose, the DLIR-H DP images had similar overall image quality to the routine-dose ASIR-V50% AP images. [ABSTRACT FROM AUTHOR]