Single Image Super-Resolution via Wide-Activation Feature Distillation Network.

Feature extraction plays a pivotal role in the context of single image super-resolution. Nonetheless, relying on a single feature extraction method often undermines the full potential of feature representation, hampering the model's overall performance. To tackle this issue, this study introduces the wide-activation feature distillation network (WFDN), which realizes single image super-resolution through dual-path learning. Initially, a dual-path parallel network structure is employed, utilizing a residual network as the backbone and incorporating global residual connections to enhance feature exploitation and expedite network convergence. Subsequently, a feature distillation block is adopted, characterized by fast training speed and a low parameter count. Simultaneously, a wide-activation mechanism is integrated to further enhance the representational capacity of high-frequency features. Lastly, a gated fusion mechanism is introduced to weight the fusion of feature information extracted from the dual branches. This mechanism enhances reconstruction performance while mitigating information redundancy. Extensive experiments demonstrate that the proposed algorithm achieves stable and superior results compared to the state-of-the-art methods, as evidenced by quantitative evaluation metrics tests conducted on four benchmark datasets. Furthermore, our WFDN excels in reconstructing images with richer detailed textures, more realistic lines, and clearer structures, affirming its exceptional superiority and robustness.

Dual-Path Large Kernel Learning and Its Applications in Single-Image Super-Resolution.

To enhance the performance of super-resolution models, neural networks frequently employ module stacking. However, this approach inevitably results in an excessive proliferation of parameter counts and information redundancy, ultimately constraining the deployment of these models on mobile devices. To surmount this limitation, this study introduces the application of Dual-path Large Kernel Learning (DLKL) to the task of image super-resolution. Within the DLKL framework, we harness a multiscale large kernel decomposition technique to efficiently establish long-range dependencies among pixels. This network not only maintains excellent performance but also significantly mitigates the parameter burden, achieving an optimal balance between network performance and efficiency. When compared with other prevalent algorithms, DLKL exhibits remarkable proficiency in generating images with sharper textures and structures that are more akin to natural ones. It is particularly noteworthy that on the challenging texture dataset Urban100, the network proposed in this study achieved a significant improvement in Peak Signal-to-Noise Ratio (PSNR) for the ×4 upscaling task, with an increase of 0.32 dB and 0.19 dB compared with the state-of-the-art HAFRN and MICU networks, respectively. This remarkable result not only validates the effectiveness of the present model in complex image super-resolution tasks but also highlights its superior performance and unique advantages in the field.

Coal Flow Foreign Body Classification Based on ESCBAM and Multi-Channel Feature Fusion.

Foreign bodies often cause belt scratching and tearing, coal stacking, and plugging during the transportation of coal via belt conveyors. To overcome the problems of large parameters, heavy computational complexity, low classification accuracy, and poor processing speed in current classification networks, a novel network based on ESCBAM and multichannel feature fusion is proposed in this paper. Firstly, to improve the utilization rate of features and the network's ability to learn detailed information, a multi-channel feature fusion strategy was designed to fully integrate the independent feature information between each channel. Then, to reduce the computational amount while maintaining excellent feature extraction capability, an information fusion network was constructed, which adopted the depthwise separable convolution and improved residual network structure as the basic feature extraction unit. Finally, to enhance the understanding ability of image context and improve the feature performance of the network, a novel ESCBAM attention mechanism with strong generalization and portability was constructed by integrating space and channel features. The experimental results demonstrate that the proposed method has the advantages of fewer parameters, low computational complexity, high accuracy, and fast processing speed, which can effectively classify foreign bodies on the belt conveyor.

[An Improved Object Detection Algorithm for Thyroid Nodule Ultrasound Image Based on Faster R-CNN].

Objective: To propose an improved algorithm for thyroid nodule object detection based on Faster R-CNN so as to improve the detection precision of thyroid nodules in ultrasound images. Methods: The algorithm used ResNeSt50 combined with deformable convolution (DC) as the backbone network to improve the detection effect of irregularly shaped nodules. Feature pyramid networks (FPN) and Region of Interest (RoI) Align were introduced in the back of the trunk network. The former was used to reduce missed or mistaken detection of thyroid nodules, and the latter was used to improve the detection precision of small nodules. To improve the generalization ability of the model, parameters were updated during backpropagation with an optimizer improved by Sharpness-Aware Minimization (SAM). Results: In this experiment, 6 261 thyroid ultrasound images from the Affiliated Hospital of Xuzhou Medical University and the First Hospital of Nanjing were used to compare and evaluate the effectiveness of the improved algorithm. According to the findings, the algorithm showed optimization effect to a certain degree, with the AP50 of the final test set being as high as 97.4% and AP@50:5:95 also showing a 10.0% improvement compared with the original model. Compared with both the original model and the existing models, the improved algorithm had higher detection precision and improved capacity to detect thyroid nodules with better accuracy and precision. In particular, the improved algorithm had a higher recall rate under the requirement of lower detection frame precision. Conclusion: The improved method proposed in the study is an effective object detection algorithm for thyroid nodules and can be used to detect thyroid nodules with accuracy and precision.

An analysis on efficacy of applying ß-elemene intervention on chemically -induced tongue lesions using SAM algorithm.

An artificial intelligence (AI) model was designed to assist pathologists in diagnosing and quantifying structural changes in tongue lesions induced by chemical carcinogens. Using a tongue cancer model induced by 4-nitroquinoline-N-oxide and treated with ß-elemene, a total of 183 digital pathology slides were processed. The Segment Anything Model (SAM) was employed for initial segmentation, followed by conventional algorithms for more detailed segmentation. The epithelial contour area was computed using OpenCV's findcontour method, and the skeletonize method was used to calculate the distance map and skeletonized representation. The AI model demonstrated high accuracy in measuring tongue epithelial thickness and the number of papilla-like protrusions. Results indicated that the model group had significantly higher epithelial thickness and fewer papillae compared with the blank group. Furthermore, the treatment group exhibited reduced epithelial thickness and fewer papilla-like protrusions compared with the model group, though these differences were less pronounced. Overall, the SAM framework algorithm proved effective in quantifying tongue epithelial thickness and the number of papilla-like protrusions, thereby assisting healthcare professionals in understanding pathological changes and assessing treatment outcomes.

3D Reconstruction Method based on Medical Image Feature Point Matching.

Medical 3D image reconstruction is an important image processing step in medical image analysis. How to speed up the speed while improving the accuracy in 3D reconstruction is an important issue. To solve this problem, this paper proposes a 3D reconstruction method based on image feature point matching. By improving SIFT, the initial matching of feature points is realized by using the neighborhood voting method, and then the initial matching points are optimized by the improved RANSAC algorithm, and a new SFM reconstruction method is obtained. The experimental results show that the feature matching rate of this algorithm on Fountain data is 95.42% and the matching speed is 4.751 s. It can be seen that this algorithm can shorten the reconstruction time and obtain sparse point clouds with more reasonable distribution and better reconstruction effect.

Numerical simulation of hemodynamics in membranous obstruction of the suprahepatic inferior vena cava based on a subject-specific Budd-Chiari syndrome model.

BACKGROUND: This study was performed to determine the hemodynamic changes of Budd-Chiari syndrome when the inferior vena vein membrane is developing. METHODS: A patient-specific Budd-Chiari syndrome vascular model was reconstructed based on magnetic resonance images using Mimics software and different degrees (16%, 37%, and 54%) of idealized membrane were built based on the Budd-Chiari syndrome vascular model using Geomagic software. Three membrane obstruction Budd-Chiari syndrome vascular models were established successfully and fluent software was used to simulate hemodynamic parameters, including blood velocity and wall shear stress. FINDINGS: The simulation results showed that there is low velocity and a low wall shear stress region at the junction of the inferior vena cava and the branches of the hepatic veins, and swirl may occur in this area. As the membrane develops, the size of the low velocity and low wall shear stress regions enlarged and the wall shear stress was increased at the membrane region. There was a significant difference in the mean values of wall shear stress between the different obstruction membrane models (P<0.05). INTERPRETATION: Hemodynamic parameters play an important role in vascular disease and there may be a correlation between inferior vena cava wall shear force changes and the slow development process of the inferior vena cava membrane.

Arabidopsis thaliana as a suitable model host for research on interactions between plant and foliar nematodes, parasites of plant shoot.

The rice white tip nematode (RWTN), Aphelenchoides besseyi and the chrysanthemum foliar nematode (CFN), Aphelenchoides ritzemabosi are migratory plant parasitic nematodes that infect the aboveground parts of plants. In this research, Arabidopsis thaliana was infected by RWTN and CFN under indoor aseptic cultivation, and the nematodes caused recognizable symptoms in the leaves. Furthermore, RWTN and CFN completed their life cycles and proliferated. Therefore, A. thaliana was identified as a new host of RWTN and CFN. The optimum inoculum concentration for RWTN and CFN was 100 nematodes/plantlet, and the optimum inoculum times were 21 and 24 days, respectively. For different RWTN populations, the pathogenicity and reproduction rates were different in the A. thaliana Col-0 ecotype and were positively correlated. The optimum A. thaliana ecotypes were Col-0 and WS, which were the most susceptible to RWTN and CFN, respectively. Additionally, RWTN was ectoparasitic and CFN was ecto- and endoparasitic in A. thaliana. The RWTN and CFN migrated from inoculated leaves to the entire plantlet, and the number of nematodes in different parts of A. thaliana was not correlated with distance from the inoculum point. This is a detailed study of the behavior and infection process of foliar nematodes on A. thaliana.