To address the challenges associated with methane gas leak monitoring and localization in oil and gas pipelines, and to overcome the limitations of existing laser-based point measurement techniques, this study proposes a novel dual-band short-wave infrared (SWIR) gas detection technology based on differential ratio spectral imaging. The system locks the laser output at a central wavelength specific to methane detection and controls the laser beam scanning trajectory and direction in real time through an adjustable beam scanning module. By integrating laser absorption spectroscopy with a shortwave infrared camera, spectral information from both absorption and non-absorption regions is captured to generate intensity images of methane gas at varying volume fractions under specific laser wavelengths. Through the application of image processing algorithms, differential ratio analysis is conducted between the methane absorption band (1653.72 nm) and two adjacent non-absorption bands (1653.82 nm and 1653.62 nm), effectively eliminating background radiation interference in methane gas plumes and distinctly visualizing the intensity distribution of methane volume fractions. Experimental validation using standard gas bags to simulate methane leak scenarios demonstrates the feasibility of this approach, with processed image intensity to establish a calibration curve correlating the dual-band differential ratio with gas concentration path length, enabling quantitative detection and analysis. This imaging methodology effectively mitigates the drawbacks of conventional measurement techniques, such as measurement point deviation and the inability to achieve quantitative assessment, thereby providing a robust and precise approach for applications including methane cloud distribution mapping in mining operations and methane leak detection in oil and gas fields.

A distributed optical fiber sensing system composed of communication optical cables, strain optical cables, and a Brillouin optical time-domain reflectometer is adopted to conduct long-term monitoring of the slope stability of open-pit mines. By optimizing the trench structure design, anchoring system, and optical cable laying path, the optical cable deployment for 10 km, 3300 m, and 3500 m of the open-pit mine slope is completed, establishing a distributed monitoring engineering verification system for open-pit mine slopes. The research results show that through reasonable engineering design and control of the test parameters of the Brillouin optical time-domain reflectometer, long-distance, continuous, real-time, and large-scale distributed monitoring can be achieved; rock and soil masses, anchor rods, and strain optical cables can achieve a better coupling effect, which can realize the spatial?temporal continuous perception of surface and internal deformation of open-pit mine slopes. This method provides valuable insights into the deformation behavior of rock and soil mass slopes. In the future, by combining this monitoring technology with methods such as numerical simulation, potential landslide risks can be effectively and timely identified, and necessary measures can be implemented to ensure the stability of the slope.

In view of the problems of large number of original data layers in computed tomography (CT) image acquisition and ease errors in the reconstruction process, we propose an improved FBP algorithm, which is integrated with Amira software for the 3D reconstruction of CT images. First, the improved Ram?Lak filtering is applied to CT images using MATLAB software for filtering. Second, the CT images are also binarized and cropped for preprocessing, the improved FBP algorithm is used to reconstruct two-dimensional CT images and generate multiple tomographic images. A comparison is made between the reconstruction results of the traditional FBP algorithm and the improved FBP algorithm. Finally, Amira software is employed to perform a three-dimensional reconstruction of the multiple tomographic images. The experimental results show that the proposed method can effectively solve problems such as redundant data collection, large spatial crosstalk, poor imaging effect, and high radiation dose in the actual clinical CT imaging process. Moreover, the obtained tomographic image edges are clearer, and the reconstructed 3D image of the finger is more intuitive and stereoscopic.

Super-resolution microscopy has broken through the diffraction limit of conventional optical microscopy, providing a crucial tool for high-resolution optical imaging at the sub-cellular scale. This breakthrough has significantly advanced research in frontier disciplines such as cell biology, neuroscience, and pathology. Among various super-resolution techniques, structured illumination super-resolution microscopy (SIM) technology has emerged as a vital method for studying the dynamics of fine structures in live cells, owing to its rapid imaging capability, low phototoxicity, and excellent compatibility with fluorescent probes. With the rapid development of artificial intelligence, deep learning has injected new momentum into SIM technology. Deep learning-enhanced SIM has achieved groundbreaking progress in reducing phototoxicity, increasing imaging speed, improving resolution, and expanding functional applications, greatly broadening its scope. This review systematically summarizes recent advances in deep learning-driven SIM technology and provides a perspective on future developments in the field.

To address the requirement for efficient registration and fusion of unregistered infrared and visible images, we propose RRFNet-DCDAN, a unified infrared?visible image registration and fusion network incorporating a dynamic convolutional dual attention mechanism. This model enables adaptive fusion of unregistered multimodal images with different resolutions. RRFNet-DCDAN combines a dual attention network (DAN) and dynamic convolution attention (DCA) module. The DAN is employed to emphasize the salient features of infrared targets and preserve the texture details of visible images. Simultaneously, the DCA efficiently handles input image pairs with different resolutions. Based on the MSRS and RoadScene datasets, the proposed method outperforms other benchmark models (e.g., GTF and DenseFuse) in five evaluation metrics (e.g., mutual information and visual fidelity). Meanwhile, the image processing time is reduced by 20.57%. The proposed method provides a promising way for processing images with different resolutions.

Aiming at the problem of missing point cloud data when measuring highly reflective surfaces with traditional sinusoidal stripe structured light, this paper proposes a 3D reconstruction method of highly reflective surfaces based on binary encoded stripes. Firstly, half-periodic sinusoidal stripes are used to replace the traditional sinusoidal stripes and binary coding is applied to them, and only binary stripes need to be projected during the whole measurement process, which avoids the complex nonlinear correction process and enhances the system's anti-interference ability. Secondly, the number of exposures is reduced by the introduction of the quantized exposure time selection strategy, which shortens the measurement time. Lastly, the high-dynamic-range images are acquired by combining with image mask fusion technology. The experimental results show that the number of complementary point cloud voids is 20853 and the growth rate of the point cloud is 8.45% using the method of this paper for the reconstruction of highly reflective surfaces of ceramic cup, and the proposed method has a certain degree of universality for different material objects.

Characterization of photodetectors (PDs) plays a crucial guiding role in device design and optimization. Therefore, there is an urgent need to develop measurement techniques capable of realizing the small-signal linear and large-signal nonlinear frequency responses of PDs, in order to comprehensively evaluate and understand the performance of the PDs. We propose a high-frequency characterization method for PDs based on pilot-tone modulation. When measuring the small-signal linear frequency response, pilot-tone tags are used for the decoupling of the electro-optical modulation frequency response, and the scalable high-frequency characterization of the small-signal frequency response of PDs is achieved through harmonic up-conversion of a narrowband electro-optical stimulus. When measuring the large-signal nonlinear frequency response (third-order intermodulation distortion), electro-optical modulation spurious is decoupled by means of the pilot-tone tags, thereby realizing modulation-spurious-free and frequency-swept characterization of the large-signal nonlinear frequency response of PDs. This provides a technical solution for the dynamic frequency response characterization of high-speed, high-power, and high-linearity PDs.