In order to solve the problems of low detection accuracy and poor detection effect caused by the characteristics of multiple defects of transmission line insulators, such as small size and complex background, an improved insulator multi-defect detection algorithm based on YOLO was proposed. Firstly, the FasterNet module was introduced to replace the original module backbone network to reduce the number of model parameters. In terms of feature extraction, the scale-sequence feature fusing module (SSFF) is introduced to combine the high-level information of the deep feature map with the detailed information of the shallow feature map, fully integrate the multi-scale features, and improve the distinction of different defects of the insulator. Finally, a triple feature encoding module (TFE) was introduced to better capture the details of small targets and enhance the recognition of overlapping and small targets. Experimental results show that the average accuracy of the algorithm (mAP0.5) is increased by 3.8
Dynamic Causal Modeling (DCM) is a crucial tool for studying brain effective connectivity, offering valuable insights into brain network dynamics through functional magnetic resonance imaging (fMRI) and electrophysiology (EEG and MEG). However, its high computational complexity limits its applicability in large-scale network analysis. To address this issue, we propose a regression algorithm that integrates the Generalized Linear Model (GLM) with Sparse DCM, termed GSD. This algorithm enhances computational performance through three key optimizations: (1) utilizing the symmetry of the Fourier transform to convert complex frequency domain calculations into real number operations, thereby reducing computational complexity; (2) applying GLM and filtering techniques to minimize the effects of noise and confounds, enhancing parameter estimation accuracy; and (3) defining a new cost function to optimize variational inference and filter parameters, further improving parameter estimation accuracy. We validated the GSD algorithm using three public fMRI datasets: simulated Smith small-world network data, attention and motion measured data, and face recognition repetition effect measured data. The experimental results demonstrate that the GSD algorithm reduces computation time by over 50% while maintaining parameter estimation performance comparable to traditional methods. These findings offer a new perspective on balancing model interpretability and computational efficiency, potentially broadening the application of DCM across various fields.
Due to the influence of rainy and foggy weather, obtaining clear images becomes more challenging, often resulting in low visibility, poor contrast, and missing detail information. To address these issues, a robust image defogging algorithm is proposed. Firstly, the input image undergoes conversion into a detailed image, with attenuation and redefinition of its three color channels. Color compensation and balance are then applied based on the principle of minimizing color loss. Secondly, the problem of image darkening is tackled through an improved atmospheric scattering model (EASM) and the dark channel a priori algorithm. The defogging results exhibit noticeable enhancements in terms of bright colors and clear details. In the natural images showcased in the paper, the proposed algorithm achieves improvements in information entropy, the fog density evaluator (FADE), and the natural image quality evaluator (NIQE) by 0.46%, 9.7%, and 12.0%, respectively, compared to the suboptimal algorithm. In the synthetic image datasets I-HAZE and O-HAZE, there are enhancements in information entropy by 0.19% and 0.76%, respectively, and in NIQE by 1.05% each, albeit slightly lower than the sub-optimal results. The structural similarity (SSIM) also sees improvements of 6.3% and 10.9% compared to the suboptimal results in FADE. These findings demonstrate the superior performance of the proposed algorithm over the latest defogging algorithms in terms of information entropy, FADE, NIQE, and SSIM, underscoring its high robustness and promising application prospects.
Chinese character skeletons are key topological description of Chinese character glyphs which provided essential information about structures of the Chinese characters. The Chinese character skeleton can be regarded as sequential points generated using deep neural networks, such as recurrent neural networks (RNN) while time intervals between sequential points which included dynamic information of handwriting process of Chinese characters could not be presented elegantly using RNN. Deep neural networks with long sequence outputs face problems such as gradient vanishing or explosion. Chinese characters had longer sequential points than those of English characters. Here, we generated the Chinese character skeletons using multiple parallel random recurrent networks (RRN) which provided rich dynamic presentation of sequential points. The generation took place at two levels, the stroke level which included a sequence of points, and the character level which included a sequence of strokes. Our method not only generated temporal information of the skeletons but also retained more skeleton feature points with only small-scale training data which made the generated Chinese character skeletons had richer writing detail features than existing methods. Our method avoided the problems of gradient vanishing or explosion, and could be used to quickly generate large-scale and high-quality Chinese character skeletons.
Recent years have witnessed the prosperity of reference-based image super-resolution (Ref-SR). By importing the high-resolution (HR) reference images into the single image super-resolution (SISR) approach, the ill-posed nature of this long-standing field has been alleviated with the assistance of texture transferred from reference images. Although the significant improvement in quantitative and qualitative results has verified the superiority of Ref-SR methods, the presence of misalignment before texture transfer indicates room for further performance improvement. Existing methods tend to neglect the significance of details in the context of comparison, therefore not fully leveraging the information contained within low-resolution (LR) images. In this paper, we propose a Detail-Enhancing Framework (DEF) for reference-based super-resolution, which introduces the diffusion model to generate and enhance the underlying detail in LR images. If corresponding parts are present in the reference image, our method can facilitate rigorous alignment. In cases where the reference image lacks corresponding parts, it ensures a fundamental improvement while avoiding the influence of the reference image. Extensive experiments demonstrate that our proposed method achieves superior visual results while maintaining comparable numerical outcomes.
