An automatic thresholding method based on Shannon entropy difference and dynamic synergic entropy is proposed to select a reasonable threshold from the gray level image with a unimodal, bimodal, multimodal, or peakless gray level histogram. Firstly, a new concept called Shannon entropy difference is proposed, and the stopping condition of a multi-scale multiplication transformation is automatically controlled by maximizing Shannon entropy difference to produce edge images. Secondly, the gray level image is thresholded by the gray levels in order from smallest to largest to generate a series of binary images, followed by extracting contour images from the binary images. Then, a series of gray level histograms that can dynamically reflect gray level distributions and pixel positions are constructed using the edge images and the contour images synergically. Finally, dynamic synergic Shannon entropy is calculated from this series of gray level histograms, and the threshold corresponding to maximum dynamic synergic entropy is taken as the final segmentation threshold. The experimental results on 40 synthetic images and 50 real-world images show that, although the proposed method is not superior to 8 automatic segmentation methods in computational efficiency, it has more flexible adaptivity of selecting threshold and better segmentation accuracy.

We have developed a compact high-resolution retarding field energy analyzer for measuring the energy spread of space-charge-dominated electron beams. This energy analyzer has a cylindrical electrode to overcome the defocusing effects due to space-charge forces, beam trajectories, aperture effect, etc. The device provides excellent spatial and temporal information on the beam energy spread. Single-particle simulation shows that this energy analyzer has very good resolution for low-energy electron beams of several kilovolts and with large divergence angles. The energy analyzer has been tested with 2.5 keV, 60 mA electron beams. The measured energy spread is also compared with the theoretical calculations taking into account two main energy spread sources, namely, the Boersch effect and the longitudinal-longitudinal relaxation.

Thermal infrared image super-resolution technology successfully solves the problems of low resolution and blurred texture details in infrared images. However, the problem of background thermal noise and streak interference in thermal infrared images has not been effectively solved. Therefore, in this paper, we analyze and model the generation of background thermal noise and streak interference, and propose a real-world super-resolution algorithm based on generative adversarial network with multi-structure fusion. We first statistically analyze the imaging principle and dataset of the thermal imager to better model the phenomenon of background thermal noise and streak interference present in thermal infrared images. Meanwhile, in order to better recover the details, we use grayed-out visible images to guide the network training and propose a novel generator with multi-structural fusion. In the generator, we design a dynamic dense-attention module that dynamically assigns weights to the attention branch and the densely connected branch to take full advantage of both branches. Compared to other state-of-the-art methods, our proposed method exhibits excellent visual effects, effectively eliminating the effects of noise and streaks while enhancing image texture information.

Most of the existing RGB-D saliency detectors have tried different strategies to fuse RGB and depth information to generate better saliency detection results. However, the ability of only using RGB image to detect the salient object is ignored in RGB-D saliency detectors. In this paper, we propose a triple-complementary network for fully exploring the RGB and depth information. The first two sub-networks are used for extracting saliency from RGB image and RGB-D image pair, respectively. The third sub-network refines the saliency map by comprehensively considering RGB image, depth map and the refined saliency map of the first two sub-networks. We also propose depth weighted refinement to suppress the high contrast objects with large depth values. The proposed method not only fully explores the information of RGB but also makes the RGB and depth tightly coupled. The experiments on five RGB-D saliency detection datasets show the superiority of the proposed method over the state-of-the-art RGB-D saliency detectors.

Image segmentation is a crucial step in image processing and analysis, with multi-level thresholding being one of the important techniques for image segmentation. Existing approaches predominantly rely on metaheuristic optimization algorithms, which frequently encounter local optima stagnation and require extensive parameter tuning, thereby degrading segmentation accuracy and computational efficiency. This paper proposes a Shannon entropy-based multi-level thresholding method that utilizes composite contours. The method selects appropriate multiscale multiplication images by maximizing the Shannon entropy difference and constructs a new Shannon entropy objective function by dynamically combining contour images. Ultimately, it automatically determines multiple thresholds by integrating local contour Shannon entropy. Experimental results on synthetic images and real-world images with complex backgrounds, low contrast, blurred boundaries, and unbalanced sizes demonstrate that the proposed method outperforms six recently proposed multi-level thresholding methods based on the Matthew’s correlation coefficient, indicating stronger adaptability and robustness for segmentation without requiring complex parameter tuning.