In this letter, an efficient system of hyperspectral imaging is discussed, which is based on diffractive optic imaging technology. The system is a spectrometer that projects the spectral and spatial information onto a CCD detector. Each spectral image can be obtained by modified demodulation algorithm. The system structure and the basic theory are introduced. A spectrometer system that operates in the visible band is designed. The performance of the system is analyzed and evaluated. The virtual simulation experiment of diffractive optic imaging is established. The simulation of diffractive imaging and spectral demodulation of complex scene have been finished. The experiment PSF is used to demodulate the spectral images. The demodulation output images are almost the same as the initial input image. The validity and feasibility of the basic principle are proved by the simulation experiment result. The experiment system of diffractive optic imaging in visible band is also established in the laboratory. The prototype calibration system is set up. The precise calibration system is needed to be set up in the future. The advantages of diffractive optic imaging spectrometer are no slit and high throughput. The spectrometer can be widely used in remote sensing and other fields.
With the increase in the lens aperture and widen in the spectrum, the dispersion range of diffractive optic image spectrometer (DOIS) is also growing. Large scale axial scanning increases the difficulty of system design and manufacturing of the GEO spectrometer. In this Letter, an efficient method and system for hyperspectral imaging of GEO orbit is realized by fusing diffractive optic and light field imaging technology. The emergence of the light field imaging technology provides a perfect solution for DOIS. Our system is a snapshot spectrometer that projects the spectral and spatial information simultaneously onto a CCD detector. Here a spectrometer system that operates in the 500-650nm band is designed and the performance of the system is analyzed and evaluated. Experiments are shown to illustrate the performance improvement attained by the new model. Our analysis shows that the novel snapshot hyperspectral diffractive computational image spectrometer is no-slit, high throughout, feasible and usable imager that can be widely built for many fields.
Traditional video imagers require high-speed CCD, we present a new method to implement video imagers with low speed CCD detector imager system based on video compressed. Using low speed CCD detector and transmissive liquid crystal (LC) instead of high speed CCD to get data cube; by the method of data processing method , we make high precision reconstruction of compressed video data, theoretical analysis and experimental result show that it is not ensures the video imaging quality but also reduced the frame rate of the detectors and complexity of video imaging system greatly.
The spectrometers capture large amount of raw and 3-dimensional (3D) spatial-spectral scene information with 2- dimensional (2D) focal plane arrays(FPA). In many applications, including imaging system and video cameras, the Nyquist rate is so high that too many samples result, making compression a precondition to storage or transmission. Compressive sensing theory employs non-adaptive linear projections that preserve the structure of the signal, the signal is then reconstructed from these projections using an optimization process. This article overview the fundamental spectral imagers based on compressive sensing, the coded aperture snapshot spectral imagers (CASSI) and high-resolution imagers via moving random exposure. Besides that, the article propose a new method to implement spectral imagers with linear detector imager systems based on spectrum compressed. The article describes the system introduction and code process, and it illustrates results with real data and imagery. Simulations are shown to illustrate the performance improvement attained by the new model and complexity of the imaging system greatly reduced by using linear detector.