Nonuniformity correction for internal radiation in uncooled infrared imaging based on gradient-weighted guided image filtering.

Due to the absence of a cold screen, the performance of uncooled infrared focal plane array imaging is significantly affected by internal radiation caused by optical systems or cavities during long-term operation. Previous nonuniformity correction methods have struggled to effectively eliminate fixed-pattern noise resulting from internal radiation. The paper explores an internal radiation correction method termed GWGF-IR, which utilizes a gradient-weighted guided image filter to correct spatially continuous fixed-pattern noise (FPN) in uncooled infrared focal plane array imaging systems.

Target-positioning method for a four-aperture infrared bionic compound eye based on the reprojection Kalman filter estimation.

To accurately determine the relationships among sub-aperture cameras in four-aperture infrared bionic compound eye systems and enhance the target-positioning accuracy, addressing the issues that traditional single-aperture infrared cameras suffer from a limited imaging field-of-view, and multi-aperture camera systems fail to utilize all camera combination and exhibit slow target-positioning convergence speed due to neglecting pose differences of sub-eye cameras, static and dynamic target images were captured using the system. Target-positioning methods were then designed and investigated. Spatial and pose weights were assigned based on the spatial positions and rotation angles of the cameras.

Preprocessing method for underwater time-gated SPAD-array-based images utilizing Retinex and BM3D.

Underwater optical imaging is affected by light absorption and backscattering in water, thus yielding low signal-to-noise ratios and limited imaging ranges. This study proposes an image preprocessing method for underwater, time-gated, single-photon avalanche diode (TG-SPAD)-array-based images according to the Retinex theory, and a block-matching and 3D filtering (BM3D) algorithm to address uneven illumination and complex noise issues in small-diameter, light beam, underwater imaging. Specifically, images undergo rapid illumination correction in combination with time-domain transformation.

Crosstalk model of division of focal plane polarization cameras: optical causes and parameter effects.

The division of focal plane polarization cameras is a widely used integrated polarization imaging system, where light intensity crosstalk between micro-polarizer pixels introduces measurement errors. Previous studies have only assessed qualitative crosstalk trends. Here, we model crosstalk as near-field, non-paraxial diffraction of an ideal metal grating.

Molecular identification and reproductive function of spexin in the big-belly seahorse (Hippocampus abdominalis).

Limited data are available regarding reproductive endocrinology of seahorse. Here, we reported the potential function of spexin (SPX1) in the reproduction of seahorse. SPX1, also known as neuropeptide Q (NPQ), is a novel neuropeptide that coevolved with galanin and kisspeptin.

Dual-Encoder UNet-Based Narrowband Uncooled Infrared Imaging Denoising Network.

Uncooled infrared imaging systems have significant potential in industrial hazardous gas leak detection. However, the use of narrowband filters to match gas spectral absorption peaks leads to a low level of incident energy captured by uncooled infrared cameras. This results in a mixture of fixed pattern noise and Gaussian noise, while existing denoising methods for uncooled infrared images struggle to effectively address this mixed noise, severely hindering the extraction and identification of actual gas leak plumes.

Total variational noise reduction method for EBAPS image based on weighted nuclear norm minimization.

The electron-bombarded active pixel sensor (EBAPS) is a highly sensitive vacuum-solid hybrid low-light imaging device capable of functioning in ultra-low illumination environments as low as 10-4 lx. However, this high sensitivity also causes problems, such as a low signal-to-noise ratio and complex noise. To enhance the quality of low-light night vision images captured by EBAPS and achieve effective imaging in ultra-low illumination, this study proposes a noise reduction algorithm based on the noise characteristics of EBAPS images.

Fall Detection Method for Infrared Videos Based on Spatial-Temporal Graph Convolutional Network.

The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated with traditional visual sensor-based fall detection. We propose an infrared video-based fall detection method utilizing spatial-temporal graph convolutional networks (ST-GCNs) to address these challenges.

Underwater Single-Photon 3D Reconstruction Algorithm Based on K-Nearest Neighbor.

The high sensitivity and picosecond time resolution of single-photon avalanche diodes (SPADs) can improve the operational range and imaging accuracy of underwater detection systems. When an underwater SPAD imaging system is used to detect targets, backward-scattering caused by particles in water often results in the poor quality of the reconstructed underwater image. Although methods such as simple pixel accumulation have been proven to be effective for time-photon histogram reconstruction, they perform unsatisfactorily in a highly scattering environment.

A Simulation Method for Underwater SPAD Depth Imaging Datasets.

In recent years, underwater imaging and vision technologies have received widespread attention, and the removal of the backward-scattering interference caused by impurities in the water has become a long-term research focus for scholars. With the advent of new single-photon imaging devices, single-photon avalanche diode (SPAD) devices, with high sensitivity and a high depth resolution, have become cutting-edge research tools in the field of underwater imaging. However, the high production costs and small array areas of SPAD devices make it very difficult to conduct underwater SPAD imaging experiments.

Real-time binocular visual localization system based on the improved BGNet stereo matching framework.

Binocular vision technology is widely used to acquire three-dimensional information of images because of its low cost. In recent years, the use of deep learning for stereo matching has shown promising results in improving the measurement stability of binocular vision systems, but the real-time performance in high-precision networks is typically poor. Therefore, this study constructed a deep-learning-based stereo matching binocular vision system based on the BGLGA-Net, which combines the advantages of past networks.

A Binocular Stereo-Imaging-Perception System with a Wide Field-of-View and Infrared- and Visible Light-Dual-Band Fusion.

With the continuous evolution of autonomous driving and unmanned driving systems, traditional limitations such as a limited field-of-view, poor ranging accuracy, and real-time display are becoming inadequate to satisfy the requirements of binocular stereo-perception systems. Firstly, we designed a binocular stereo-imaging-perception system with a wide-field-of-view and infrared- and visible light-dual-band fusion. Secondly we proposed a binocular stereo-perception optical imaging system with a wide field-of-view of 120.

Iterative Network for Disparity Prediction with Infrared and Visible Light Images Based on Common Features.

In recent years, the range of applications that utilize multiband imaging has significantly expanded. However, it is difficult to utilize multichannel heterogeneous images to achieve a spectral complementarity advantage and obtain accurate depth prediction based on traditional systems. In this study, we investigate CFNet, an iterative prediction network, for disparity prediction with infrared and visible light images based on common features.

Calibration method for thermal infrared division-of-focal-plane polarimeters considering polarizer reflection characteristics.

Owing to manufacturing defects of micropolarizer arrays and differences in the pixel response of detectors, division-of-focal-plane (DoFP) polarimeters have severe nonuniformity, which affects the measurement accuracy of the polarimeters and the calculation of the polarization information. This study proposes a calibration method for thermal infrared DoFP polarimeters considering polarizer reflection characteristics. The temperature-controlled adjustable infrared polarized radiation source is calibrated by a division-of-time polarimeter and is, in turn, used to calibrate a thermal infrared DoFP polarimeter.

A Low-Delay Dynamic Range Compression and Contrast Enhancement Algorithm Based on an Uncooled Infrared Sensor with Local Optimal Contrast.

Real-time compression of images with a high dynamic range into those with a low dynamic range while preserving the maximum amount of detail is still a critical technology in infrared image processing. We propose a dynamic range compression and enhancement algorithm for infrared images with local optimal contrast (DRCE-LOC). The algorithm has four steps.

Self-calibration algorithm for installation angle deviation of bionic polarization compound eyes.

A self-calibration algorithm based on unsupervised optimization for polarizer installation angle deviation is proposed and used in a multi-aperture bionic polarization compound eye system. To simplify calibration operation, under the condition that the calibration-polarized light information is unknown, this algorithm fully exploits redundancy and random polarization information in the scene, and uses a non-convex multi-objective discrete parameter sorting optimization method to achieve angle self-calibration. Compared with ordinary calibration procedures, the algorithm requires less stringent conditions, achieves online calibration and is more accurate.

Baseline correction for Raman spectra using a spectral estimation-based asymmetrically reweighted penalized least squares method.

Baseline correction is necessary for the qualitative and quantitative analysis of samples because of the existence of background fluorescence interference in Raman spectra. The asymmetric least squares (ALS) method is an adaptive and automated algorithm that avoids peak detection operations along with other user interactions. However, current ALS-based improved algorithms only consider the smoothness configuration of regions where the signals are greater than the fitted baseline, which results in smoothing distortion.

Automatic Suppression Method for Water Surface Glints Using a Division of Focal Plane Visible Polarimeter.

To address the problem of water surface detection imaging equipment being susceptible to water surface glints, this study demonstrates a method called De-Glints for suppressing glints and obtaining clear underwater images using a division of focal plane (DoFP) polarimeter. Based on the principle of polarization imaging, the best polarization angle and the image corresponding to the minimal average gray level of each pixel are calculated. To evaluate the improvement in image quality, the index was designed.

High-Speed Rail Tunnel Panoramic Inspection Image Recognition Technology Based on Improved YOLOv5.

In order to meet the fast and accurate automatic detection requirements of equipment maintenance in railway tunnels in the era of high-speed railways, as well as adapting to the high dynamic, low-illumination imaging environment formed by strong light at the tunnel exit, we propose an automatic inspection solution based on panoramic imaging and object recognition with deep learning. We installed a hyperboloid catadioptric panoramic imaging system on an inspection vehicle to obtain a large field of view as well as to shield the high dynamic phenomena at the tunnel exit, and proposed a YOLOv5-CCFE object detection model based on railway equipment recognition. The experimental results show that the mAP@0.

InAs nanowire arrays for room-temperature ultra-broadband infrared photodetection.

Detection of short-wave infrared (SWIR) and mid-wave infrared (MWIR) emissions remains challenging despite their importance in many emerging applications, including night vision, space imaging and remote sensing. III-V compound semiconductor materials such as InAs have an ideal band gap covering a spectral regime from near-infrared (NIR), SWIR to MWIR. However, due to their high dark current, InAs photodetectors normally require a low-temperature operation, which has greatly limited their practical applications.

Small Zoom Mismatch Adjustment Method for Dual-Band Fusion Imaging System Based on Edge-Gradient Normalized Mutual Information.

Currently, automatic optical zoom setups are being extensively explored for their applications in search, detection, recognition, and tracking. In visible and infrared fusion imaging systems with continuous zoom, dual-channel multi-sensor field-of-view matching control in the process of synchronous continuous zoom can be achieved by pre-calibration. However, mechanical and transmission errors of the zoom mechanism produce a small mismatch in the field of view after co-zooming, degrading the sharpness of the fusion image.

Unsupervised convolutional variational autoencoder deep embedding clustering for Raman spectra.

Unsupervised deep learning methods place increased emphasis on the process of cluster analysis of unknown samples without requiring sample labels. Clustering algorithms based on deep embedding networks have been recently developed and are widely used in data mining, speech processing and image recognition, but barely any of them have been used on spectra data. This study presents an unsupervised clustering algorithm for Raman spectra, called the convolutional variational autoencoder deep embedding clustering method (CVDE).

Multi-Integration Time Adaptive Selection Method for Superframe High-Dynamic-Range Infrared Imaging Based on Grayscale Information.

With the development of superframe high-dynamic-range infrared imaging technology that extends the dynamic range of thermal imaging systems, a key issue that has arisen is how to choose different integration times to obtain an HDR fusion image that contains more information. This paper proposes a multi-integration time adaptive method, in order to address the lack of objective evaluation methods for the selection of superframe infrared images, consisting of the following steps: image evaluation indicators are used to obtain the best global exposure image (the optimal integration time); images are segmented by region-growing point to obtain the ambient/high-temperature regions, selecting the local optimum images with grayscale closest to the medium grayscale of the IR imaging system for the two respective regions (lowest and highest integration time); finally, the three images above are fused and enhanced to achieve HDR infrared imaging. By comparing this method with some existing integration time selection methods and applying the proposed method to some typical fusion methods, via subjective and objective evaluation, the proposed method is shown to have obvious advantages over existing algorithms, and it can optimally select the images from different integration time series images to form the best combination that contains full image information, expanding the dynamic range of the IR imaging system.

Residual Interpolation Integrated Pixel-by-Pixel Adaptive Iterative Process for Division of Focal Plane Polarimeters.

Residual interpolations are effective methods to reduce the instantaneous field-of-view error of division of focal plane (DoFP) polarimeters. However, their guide-image selection strategies are improper, and do not consider the DoFP polarimeters' spatial sampling modes. Thus, we propose a residual interpolation method with a new guide-image selection strategy based on the spatial layout of the pixeled polarizer array to improve the sampling rate of the guide image.