In this paper, a real-time, dynamic three-dimensional (3D) shape reconstruction scheme based on the Fourier-transform profilometry (FTP) method is achieved with a short-wave infrared (SWIR) indium gallium arsenide (InGaAs) camera for monitoring applications in low illumination environments. A SWIR 3D shape reconstruction system is built for generating and acquiring the SWIR two-dimensional (2D) fringe pattern of the target. The depth information of the target is reconstructed by employing an improved FTP method, which has the advantages of high reconstruction accuracy and speed. The maximum error in depth for static 3D shape reconstruction is 1.15 mm for a plastic model with a maximum depth of 36 mm. Meanwhile, a real-time 3D shape reconstruction with a frame rate of 25 Hz can be realized by this system, which has great application prospects in real-time dynamic 3D shape reconstruction, such as low illumination monitoring. In addition, for real-time dynamic 3D shape reconstruction, without considering the edge areas, the maximum error in depth among all frames is 1.42 mm for a hemisphere with a depth of 35 mm, and the maximum error of the average of all frames in depth is 0.52 mm.

A real-time high dynamic range (HDR) imaging and display method based on correlated double sampling is proposed for short-wave infrared (SWIR) cameras in order to effectively improve its range of brightness and contrast, as well as to obtain more image details. The method utilizes the correlated double sampling technique of the SWIR detector to extend the 14-bit raw image into a 16-bit HDR image and achieve 4 times the HDR imaging. Subsequently, a dynamic range compression process, including logarithmic mapping and histogram equalization, is performed for the 16-bit HDR image to be mapped to an 8-bit display. Finally, the experimental results show that the method can enrich the details of SWIR images under the premise of ensuring real-time imaging.

<!-- *** Custom HTML *** --> Blind pixels are an inevitable problem in short-wave infrared (SWIR) InGaAs focal plane arrays (FPAs). An experiment using a laser beam-induced current method was set up to characterize the blind pixels in InGaAs FPAs. According to the result, the blind pixels are classified into four types: single overhot pixels, single dead pixels, crossing dead pixels, and paired blind pixels. Additionally, the causes of the different types of blind pixels are analyzed. It is indicated that the blind pixels are caused by defects and impurities, disconnection of the readout circuit, unformed PN junctions, and a wrong connection between the PN junction and readout integrated circuit, respectively. Based on the characterization, a blind pixel calibration method that greatly improved the quality of images taken by InGaAs FPAs is proposed.

In this paper, a modified infrared and visible image registration method based on contour feature is proposed. Our method firstly extracts the feature contour and eliminates sparkling waves contour of the sea surface, determines the main direction of the contour based on the contour image, then uses the improved Scale Invariant Feature Transform (SIFT) method as the feature point to construct the descriptor, completes the registration of the two images. 30 sets of infrared and visible-band vessels images were selected for registration experiments. Compared with previous reports, the experimental results showed that the proportion of effective feature points detected by this method can reach 70%, and the average number of effective feature points detected by proposed method can reach 196 in visible band image and 279 in infrared image. The running time was 5.3599s, shortened by 25% compared with previous reports, and the average Root Mean Square Error (RMSE) value was 2.3566, smaller by 75% compared with previous reports. An effective registration method is provided, which can be used for infrared and visible image processing and comprehensive utilization of information in marine scenes.

Optical projection tomography (OPT) is the direct optical equivalent of X-ray computed tomography (CT). To obtain a larger depth of field, traditional OPT usually decreases the numerical aperture (NA) of the objective lens to decrease the resolution of the image. So, there is a trade-off between sample size and resolution. Commercial microfluidic systems can observe a sample in flow mode. In this paper, an OPT instrument is constructed to observe samples. The OPT instrument is combined with commercial microfluidic systems to obtain a three-dimensional and time (3D + T)/four-dimensional (4D) video of the sample. “Focal plane scanning” is also used to increase the images’ depth of field. A series of two-dimensional (2D) images in different focal planes was observed and compared with images simulated using our program. Our work dynamically monitors 3D OPT images. Commercial microfluidic systems simulate blood flow, which has potential application in blood monitoring and intelligent drug delivery platforms. We design an OPT adaptor to perform OPT on a commercial wide-field inverted microscope (Olympusix81). Images in different focal planes are observed and analyzed. Using a commercial microfluidic system, a video is also acquired to record motion pictures of samples at different flow rates. To our knowledge, this is the first time an OPT setup has been combined with a microfluidic system.

The InGaAs short-wave infrared (SWIR) cameras are now widely used, but there is poor horizontal comparability between cameras. The current test and evaluation of cameras is mainly based on photon transfer theory. The specific test methods are the European Machine Vision Association (EMVA) image sensor and camera characterization test standards and the camera test protocol of teledyne photometrics (TPs). Among them, the conversion gain $K$ [e ${}^{-}$ /digital number (DN)] is a key parameter for uniformly quantifying the performance of SWIR cameras. In practical applications, a single test method is prone to deviations and fails to provide a comprehensive analysis of the error sources. In this article, we compare the theoretical foundations and experimental results of the EMVA and TP testing standards. By plotting the complete noise photon transfer curve (PTC), the mean–variance PTC, and the conversion gain curve, we conduct a comprehensive analysis of the noise and gain characteristics of the SWIR camera. Furthermore, we find that under nonideal conditions and high read noise, the results obtained from the two testing methods can validate each other, thereby determining the true conversion gain of the camera. This work contributes significantly to the in-depth understanding of camera testing standards and provides valuable insights for evaluating the performance of SWIR cameras.