The MMR (Micro Miniature Refrigerator) is a novel Joule-Thomson cryocooler manufactured by micro-machining technology, its axial length is significantly shorter than the traditional Joule-Thomson cryocoolers that are used in infrared detectors. So MMRs can greatly reduce the size of infrared detectors once they are successfully integrated. To study the working mechanism of MMRs a micro-channel flow calculation model is established considering the gas compressibility, the Joule-Thomson effect at the throttling section and the distributed Joule-Thomson effect along the micro channels and the calculation model is verified with experiments. The characteristics of heat transfer, micro-channel distribution and heat losses are further investigated, leading the model to describe the working condition of the MMR more correctly. The temperature and pressure distribution in the micro channels and the theoretical cooling performance of the MMR is thus obtained, and a set of MMR geometry parameters with superior theoretical cooling performance are acquired. Furthermore, a MMR prototype is fabricated, and a testing platform is built and the cooling performance of the prototype is experimentally studied. The experimental results agree well with the prediction of the calculation model. The MMR prototype achieves 110K and 119K cooling temperature under 10MPa N2 and Ar working condition, the cooling power reaches 231mW and 479mW, and the cool down time is 250s and 70s respectively. As a result, the cooling performance of the MMR prototype can meet the cool down requirement of infrared detectors, providing a new approach to infrared detectors’ miniaturization.
Traditional infrared and visible fused images suffer from missing details and blurred targets owing to single features in complex environments. This study presents a method for fusing infrared and visible images based on a convolution neural network（CNN） combined with a non-subsampled contourlet transform（NSCT）. Firstly, the infrared and visible target feature information is extracted by CNN, and the source image is decomposed by the NSCT at multiple scales to obtain its high-frequency coefficients and lowfrequency coefficients. Secondly, the high-frequency sub-bands and low-frequency sub-bands of the source image are fused separately using adaptive fuzzy logic and local variance contrast in combination with the target feature image. Finally, the fused image is obtained by inverse NSCT transformation. We conducted a comparative analysis with five other traditional algorithms. The experimental results show that the proposed method performs better in several objective evaluation indicators.
In order to effectively maintain and restore the edges and details of infrared images while removing the impulse noise,an iterative bilateral median filter based on intensity features and mode principle is proposed.In this method,based on the intensity features of impulse noise and the mode principle,the pixels that take the minimum and maximum values and are isolated on the intensity distribution of the neighborhood are recognized as noisy pixels.According to the weighted coefficients with respect to the spatial distance and intensity similarity,the noiseless pixels in the neighborhood and the pixels that have been denoised and restored are weighted by the frequencies,and the frequency weighted median is used as the estimated value of noisy pixels.Furthermore,the denoising processing is performed in the way of iterative traversal processing,which makes the most of the results of the previous traversal processing to remove high density noise.The experimental data confirm that the PSNR and EPI values and the visual effects achieved by the proposed method are better than the existing methods,with better denoising performance.
Quantum dots(QDs)are widely used in photoelectric detection,biomedicine,new energy,and other fields because of their quantum limitations,size,and surface effects.Recent years have seen midwave infrared(MWIR)quantum dots(QDs)become a focal point in infrared research.By adjusting and controlling their size,these QDs can extend their absorption wavelengths in the infrared spectrum.Therefore,the successful preparation of infrared QD materials and devices is crucial for infrared imaging,guidance,search,and tracking.This study first introduces the preparation and synthesis technology of five types of MWIR QDs materials,HgSe,HgTe,PbSe,Ag2Se,and HgCdTe,analyzes the size and morphology,lattice fringe,and infrared absorption spectrum characteristics of the QDs,and then summarizes the domestic and foreign MWIR QDs detectors.The device structures and preparation methods of the detector are summarized,and the photoelectric performance parameters,such as responsivity,detectivity,and response time,of the detectors are compared and analyzed.Finally,the development of MWIR QDs was discussed.
Human fall detection in infrared images is not affected by ambient light and has important research and application value in intelligent security.Existing fall detection methods do not fully consider the position change law of key points on the human body,which can easily cause false detections of similar fall movements.To solve this problem,we propose an infrared image fall detection algorithm based on an improved alpha pose.The algorithm uses the YOLO v5s object detection network to directly classify human poses while extracting the human body target frame and inputting the pose estimation network.It then evaluates it in combination with the position information and posture characteristics of the key points of the human skeleton.Experiments showed that the algorithm exhibited good performance in terms of accuracy and real-time performance.
