Aming at measure the rapid target’s spatio, temporal and spectrum characteristics data synchronously, we propose a joint spatio-temporal-spectrum super-resolution method for compressive sensing optical imaging system based DMD. Based on the CACTI and CASSI technology, we focus on the key technologies such as multi-scale joint coding and time-space-frequency multi-dimensional light field joint reconstruction algorithm using DMD. Though the accuracy of photometric characteristic data is not accurate, it can meet the requirements of non-cooperative targets emergency measurement application.
Compressed sensing theory is a new sampling theory, which provides a method to recover the original signal from a small number of samples. For sparse signal and compressible signal, compressed sensing theory compresses the signal while sampling. It combines the sampling process and compression process. It breaks through the traditional Nyquist sampling law and saves a lot of storage, transmission, computing and other resources. This theory not only reduces the cost of storage and transmission of digital image and video acquisition, but also provides a new opportunity for the follow-up research of image processing and recognition, and promotes the combination of theory and engineering application. It includes three parts: sparse representation of target, design of measurement matrix and reconstruction of target. Reconstruction algorithm is a key step in the process of compression imaging, which determines the accuracy and speed of image reconstruction to a certain extent, so it is very important to select the appropriate image quality evaluation index. The image quality evaluation of existing reconstruction algorithms mainly focuses on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM). The advantages of these two algorithms lie in simple algorithm, fast inspection speed, which are suitable for evaluating the advantages and disadvantages of algorithms, but the disadvantage is that they can only be evaluated on the basis of known original images. In the actual imaging process of compressed sensing, it is impossible to obtain the original image, so we need to use an image quality evaluation method which is not based on the original image.
The existing space situation awareness optical imaging system is limited by the satellite platform and optical system, and it is difficult to realize large aperture observation and multi-dimensional optical characteristics data acquisition for rapid target detection. Aiming at the problem that the sparse aperture system is difficult to achieve clear imaging in all depth of field, and the image quality degradation caused by the defocusing and dislocation of the object point and phase closure, the refocusing imaging technology based on light field modulation is adopted to expand the synthetic aperture to full depth of field, and effectively compress the amount of data.
Images store a lot of information and are the window for human beings to understand things. A lot of research is devoted to analyzing and processing images, which is called image processing in a broad sense. Image processing includes image recognition, image restoration, image enhancement, image coding and so on. This paper mainly focuses on the field of image restoration. Image restoration, also known as image inverse problem, aims to restore high-quality original images from degraded or damaged observations. It also acts as a preprocessing step in many intermediate and advanced image processing tasks. Due to the limitations of sensors or environmental conditions, imaging systems usually have factors such as noise, optical or motion blur, resulting in image degradation and distortion. Aiming at the ill posed problem of image pixel missing and blur in the process of compression coding, this paper uses GMM model to solve the degraded image, so as to achieve the purpose of image restoration.
Space target detection and recognition is the premise of competing for the advantage of space information and the important data source of space situation awareness. With the increasing space activities and threats such as space rendezvous and emergency launch, the demand for emergency detection and identification of space targets is becoming more and more urgent, and the demand for fast emergency and high-precision measurement of optical characteristics of space targets is also put forward. This paper introduces the space target observation information processing system based on camera array, which is being developed. The system is mainly used for space non cooperative target emergency measurement, and mainly includes four parts: firstly, the hardware basis of the whole information system is camera array optical measurement system; secondly, the system is based on STK and MATLAB two software joint platform, through the interface provided by STK/Connect and MATLAB interactive connection, through the MATLAB commands and instruction set to achieve the interactive use of data between the two software, as well as the collection and creation of charts and reports; third, the key technology of software development, namely the main function module The module mainly includes: initial orbit determination, orbit prediction, space target feature extraction based on optical characteristics, and space target recognition; finally, the whole software system is developed through the integration of the above four functional modules
Imaging lidar is widely used in the fields of national defense, military affairs and people's livelihood because of its high resolution and fast imaging speed. When the detection distance is large, the echo is relatively weak, and the echo photons impinging into each pixel of the detector array is very few, even single photon. The appearance of single photon imaging technology solves this problem. Single-photon imaging detection technology has attracted much attention as a new detection technology. In recent years, single-photon imaging radar have developed rapidly. The article introduces the working principle and key technology of single-photon imaging detection, and summarizes the development of single-photon radar. Finally, the article discusses the application of single-photon imaging detection in various fields in the future.
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