Infrared technologies provide tremendous value to our modern-day society, in the fields of remote sensing, imaging, metrology, product inspection, environmental monitoring, and biomedical diagnostics. The demand for the third-generation infrared photodetectors, to enable easy-to-fabricate, low-cost, and tunable infrared active optoelectronic materials, has driven the development of infrared colloidal quantum dots (CQDs). This review introduces the preparation methods of infrared CQDs and development of infrared CQD detectors, and lists the representative research results of CQDs in the field of optoelectronics. Finally, the infrared quantum dot photodetectors are summarized, and several research problems are proposed. The results of this study guide the commercialization of the infrared quantum dot detector.

A fast image matching algorithm based on a multiscale dense structure feature has been proposed for matching multi-sensor images. In this method, the Gabor filter is employed for generating the structure response of the image. Then, the multiscale structure feature is combined on the basis of the major orientation response. Subsequently, fast Fourier transform is employed to calculate the convolution for each feature component image in the frequency domain. Finally, the similarity between images is estimated based on the sum of the convolutions, and the position with maximum similarity is outputted as the matching result. The proposed algorithm can effectively adapt to non-linear intensity variation between a multi-sensor image and noise distortion. In the experiments, a dataset consisting of optical, infrared, and synthetic aperture radar images was used for evaluating the proposed algorithm and other existing algorithms. The results indicate that the average error matching rate of the proposed algorithm is the lowest among the investigated algorithms and it has a distinct advantage in terms of computational performance.

In this study, the You Only Look Once Version 3 (YOLOv3) target recognition algorithm in deep learning technology is used to identify the ship in an infrared image collected using an infrared imager from the sea surface. The infrared imager captures images at a frequency of up to 50 frames per second. To reduce network computing time, a few ideas are generated based on YOLOv3; additionally, a full convolution structure and the LeakReLU activation function are used to redesign a lightweight basic network to accelerate detection. The output layer uses the softmax algorithm to regress according to the characteristics of the collected infrared images, which improves the detection speed and accounts for detection accuracy.

During the long-term tracking process of a single infrared target, many technical problems occur, such as strong background interference, occlusion, deformation, and target feature attenuation. An infrared target-tracking algorithm based on tracking-learning-detection (TLD) was proposed to solve these problems. Based on compressive tracking (CT), generalized Harr-like features were replaced by histograms of oriented gradient features. In our proposed method, a complementary random measurement matrix, which extracted texture and optimized grayscale feature-weights, was introduced. Moreover, a Kalman filter, used to record the space context location information, was adopted. Hence, the tracking failure and global retrieval problem of traditional CT and TLD algorithms can be solved when the target is occluded or deformed. The infrared image-tracking algorithm based on the combination of the TLD algorithm framework and improved CT algorithm effectively solves the problem of occlusion and strong interference and improves the tracking accuracy and long-term tracking stability of the algorithm. Experimental results show that the proposed algorithm can track well in real time and maintain good accuracy and robustness in an infrared ground environment.

This study introduces a method of infrared image fusion for the infrared medium- and short-wave dual band. This method uses the single scale retinex(SSR) method for processing the short-wave image. Following this, the short-wave and medium-wave images are processed to obtain the details and background layers by average filtering and bilateral filtering, respectively. Using the weight map based on spectral residual significant and guided filter, the background and details layers of the short-wave and medium-wave images are fused to obtain the final fused image. From the results of the experimental simulation and a comparison with various fusion algorithms based on wavelet and the Laplacian pyramid, the method proposed in this work is observed to be simple and performs well in most scenarios.

The movement of the sliding guide mechanism causes an offset and deflection of the lens due to the clearance fit between the sliding guide mechanism and drawtube. Based on the theory of flexible body kinematics, the motion simulation of the sliding guide mechanism at different motion postures, length-diameter ratios and arrangement positions of the guide pin and lens, was analyzed, which influenced the lens offset and deflection. The nested steering mechanism was simulated and analyzed to verify the feasibility and meet the practical requirements of the zoom optical system. Accordingly, the infrared continuous zoom lens sliding guide mechanism was designed and fabricated. Subsequently, the zoom process of the lens was imaged. It was demonstrated that this simulation analysis provides an effective method to enable the structural design and research of infrared zoom lens.

Existing image mosaic systems exhibit the defect of blind area, which causes blind spots in the monitoring system, leads to information loss, and poses a threat to monitoring security. In order to eliminate the mosaic area resulting from the processing by the image mosaic system, a new mosaic pattern without splicing the blind area is proposed in this work. First, the advantages and disadvantages of the image stitching layout patterns with and without stitching blind areas are analyzed. Second, using the image mosaic of the images obtained from three uncooled longwave infrared cameras as an example, the design and analysis of the uncooled longwave infrared camera lens is carried out. Finally, in order to obtain a better stitched image, a corresponding treatment measure is provided for the distortion of infrared cameras.

Based on requirements of laser ranging and infrared target detection, a laser/ infrared compact optical system with a common aperture and athermalization is designed. The parameters of the optical system are defined as follows: the operating waveband covers 1.064.m of laser and 7.7-9.3.m of wavelength infrared radiation (LWIR), the diameters of the two entrance pupils are 120 mm; the focal length of the laser is 800 mm; the focal length of the LWIR is 240 mm, the F-number is 2, the field of view is 2.29°×1.83°. the vertical size of system is shortened by utilizing a catadioptric optical system with RC. The laser system shares the primary mirror and secondary mirror with the LWIR system. The beam splitting of the laser and LWIR is achieved by the secondary mirror. By using the double imaging configuration, the LWIR system exhibits nearly 100% efficiency of the cold stop. Optical passive athermalization is realized by choosing the appropriate optical materials, mechanical materials and assigning the optical power. In the temperature range of -50℃ to 70℃, the energy concentration of the laser receiving optical system is extremely high, and the image quality of the LWIR system is also quite good, which satisfies the requirements of practical applications.

In order to calibrate the color resolution of a color camera, an experimental prototype is designed which consists of a double discrete color source, a reflective collimating optical system and four-bar target panel. The dual discrete color sources respectively produce a foreground and background color beam passing through a collimating optical system, and then form a four-bar synthetic image on the color CCD. In the experiment, we extracts the stripe from the target image at each frequency and calculates the color difference of each pixel. Then, we use the criterion to mark the detectable pixels and define the minimum resolvable E difference (MRED) and the minimum detectable E difference (MDED) as two indexes to objectively evaluate the color resolution of the color camera.

In polyethylene pipe manufacturing processes, the precise measurement of the pipe thickness is an important technical problem, which affects the quality of the pipes produced. In the terahertz non-metal thickness transmission measurement process, the sample thickness parameter is obtained by measuring the optical path time difference of the two transmitted signals and material refractive index. To accurately extract the optical path time difference of the two transmitted signals, signal characterization of the original time domain signal is required, which is a mature and effective technical means to characterize the signals impulse response functions. An impulse response function can be obtained using a deconvolution technique. However, when the echo signal is weak or the system signal-to-noise ratio is low, the interference signal increases, causing distortion and flooding of the signal, and thus, the signal cannot be accurately extracted. In this study, we first pre-process the time domain signal with a Butterworth filter and perform an improved self-precision analysis on the processed signal. Second, we perform Gaussian deconvolution processing for autocorrelation signals. Finally, we obtain an improved impulse response function. The improved method aims to solve the problem of signal characterization in the field of terahertz non-metal measurement. The improved algorithm enhances the clarity of the signal and solves the distortion or flooding of the impulse response signal caused by the interference of the clutter signal. Consequently, the signal-to-noise ratio of the impulse response signal is improved.

In this study, a high-precision pulse laser ranging method, employing sinusoidal amplitude time conversion, is proposed. The method uses a sinusoidal signal as a time reference, and frequency doubling and the clock phase separation technique to control the echo pulse timing by adjusting the pulse emission delay. The signal falls within the 0-./4 interval of the reference sine wave, and then effects piecewise linear interpolation for the given interval to convert the variation of the sinusoidal amplitude into the time required for achieving high-precision ranging. The underlying principle of the method is simple and easy to implement. The experimental results show that when the average power of laser emission is 1 mW, the range accuracy is ± (5 mm + 3 ppm) within 300 m without a cooperation target.

In this paper, to improve the control and optimization of the bio-fermentation process, rapid and non-destructive detection of glycerol and butanol was studied. To this end, 80 samples of glycerol and butanol were scanned and analyzed by means of near-infrared spectroscopy. Furthermore, in order to improve the predictive ability of the model, five quantitative calibration methods, namely, the partial least squares method, interval partial least squares method, forward interval partial least squares method, backward interval partial least squares method, and minimum forward interval method were used to establish the near-infrared detection model for glycerol and butanol content. The models were then analyzed and compared. The results showed that the model established by means of the backward interval partial least squares method was effective, and the correlation coefficients of the single component solution of glycerol and butanol were 0.99932 (glycerol) and 0.98843 (butanol), respectively. To measure the content of glycerol and butanol in the glycerol fermentation broth, a monitoring platform for glycerol and butanol concentrations was established. The correlation coefficients were found to be 0.99074 (glycerol) and 0.99261 (butanol), respectively. The results show that the NIR rapid detection model demonstrates excellent performance with regards to accuracy and rapidity and provides a new detection method for the rapid detection of glycerol and butanol in the fermentation industry.

A near-infrared elbow vein image has low contrast because of low image quality, uneven illumination intensity, and thicker subcutaneous fat. Therefore, it is difficult to extract a clear vein structure. To address this problem, a multiscale adaptive vein filtering enhancement method based on the Hessian operator is proposed. The method extracts veins from an image enhanced by contrast-limited adaptive histogram equalization with the use of an improved multiscale adaptive filter. The new filter structure can adaptively determine filter parameters based on the input image and suppress noise while extracting veins. The experimental results show that the method can effectively obtain a clear and complete vein structure, and it has stronger denoising, better enhancement effects, and higher accuracy.

Breast diseases are a threat to the physical and mental health of women. The occurrence of breast cancer and mortality resulting from it ranks the highest among cancer cases in women. Hence, early detection and diagnosis of breast cancer becomes extremely important. Traditional structural imaging has some limitations in early detection of diseases, while infrared thermal imaging, as a functional imaging technology, can provide effective clues for early screening of breast cancer. This work mainly reviews the usefulness of employing infrared thermal imaging in the early detection and prognosis evaluation of breast diseases.