Computational spectral imaging integrates calculations into the spectral imaging process to achieve the purpose of improving the signal-to-noise ratio, speeding up imaging, and reducing the size of the spectrometer. At present, the commonly used evaluation methods for calculating the spectrum are mostly directly borrowed from the evaluation criteria of conventional spectral imaging, or only the spatial information evaluation method of the scene is used to evaluate the quality of the space spectrum restoration of the image. From the perspective of spectral imaging applications, the evaluation criteria for computing the quality of spectral imaging is proposed, that is spectral imaging stability is used as the first evaluation criterion. Considering that the main application field of spectral imaging is the identification of the target species and the determination of the content, the accuracy of the center position of the spectral peak of the target test set is used as the evaluation method for qualitative identification, and the trend line of the spectral curve of multiple measurements is used as the quantitative evaluation index for content determination. Two types of calculated spectral image reconstruction results are displayed. One type of mean square error is twice that of the other type. Under the evaluation criterion that the smaller the mean square error, the better the evaluation criteria, the better reconstruction results can not satisfy the spectral qualitative application. However, the method proposed in this article can quickly and effectively judge whether the restored spectrum can meet the needs of spectral analysis applications.
Airborne laser bathymetric system has great advantages in shallow sea bathymetric mapping due to its no blind area, high accuracy and high density data. By using Monte Carlo method and radiation-transport equation, the spatial distribution of the signal spot on the sea surface is calculated respectively. The results show that the spatial distribution of the signal spot returned to the sea surface is more extensive with the increase of the depth, and the power attenuation of the center of the spot is more serious. In this paper, signal to noise ratio (SNR) is used as the performance evaluation criterion of laser bathymetry system, and the requirements of field of view for signal detection under different depth are analyzed. The analytic results will provide support for the design and optimization of the laser bathymetric system.
Aiming at the problem of the inconsistency of the point spread function of each field of view of the high-resolution large-field space optical camera and the reduction of the imaging resolution caused by the influence of atmospheric interference during orbit imaging. Both the optical system design and image restoration are used to reduce the inconsistency of the point spread function of each field of view of a high-resolution large-field space optical camera. By dynamically predicting the change of the point spread function of the optical system, the impact of atmospheric interference on the spatial resolution is reduced. The experimental results show that the method used in this paper can effectively improve the imaging stability of high-resolution large-field spatial optical cameras.
The conventional diffractive optical imaging spectrometer uses the single-channel scheme, it is mainly aimed at simple targets, or gas targets with known spectral characteristics. The main disadvantage of conventional system is: if the target is a complex scene such as a natural scene, it's very difficult to demodulate spectral images accurately. Because, the focused and defocused spectral information are superimposed on each other. And, the real system has noise, manufacturing error, testing error and calibration error. So, it is difficult to correctly describe the dispersion parameters of the diffractive spectrometer, which will cause large errors of spectral demodulation accuracy. To solve this problem, an efficient system of diffractive spectral imaging is discussed, which includes a reference channel. Based on the conventional single-channel system, a grayscale camera or a color camera is added for imaging. It can provide a priori knowledge of complex scenes for the diffraction imaging channel. The data of the two channels are jointly processed to improve the final demodulation accuracy of the spectral data. The system composition and basic principles are introduced, the performance of the system is analyzed. 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 demodulation output images are almost the same as the original input image. The experiment system of diffractive optic imaging in visible band is also established in the laboratory. Theoretical analysis, imaging simulation and imaging experiment have verified the validity and feasibility of the diffraction imaging system with reference channel. Compared with the single channel system, the spectral demodulation effect is obviously improved, which greatly improves the application potential and application value.
Infrared imaging spectrometer can provide scene image information and spectral information at the same time, so as to deeply analyze the components and characteristics of the scene target. Due to the low resolution of the existing long-wave infrared imaging spectrometer filter and dispersion devices and the serious attenuation of signal energy, the time-modulated Fourier transform infrared spectrometer has a large volume and a high cost. In this paper, we propose a compact snapshot-type long-wave infrared computational spectral imaging method, which provides a new method for infrared spectral imaging and target recognition technology. Based on the coded aperture snapshot spectral imager (CASSI), we propose an imaging method that shares the main lens with two optical paths. One optical path is mainly composed of a coded mask, a relay lens, an amici prism, and a long-wave infrared detector. Its spatial and spectral resolution is determined by the encoded mask and the dispersive element. The optical system finally obtains an aliased two-dimensional image on the detector. The other optical path uses a long-wave infrared detector to provide high-resolution spatial information. Combining the two paths to obtain high-resolution infrared spectral image information through a compressed sensing reconstruction algorithm. The new spectroscopic imager described in this paper has the advantages of real-time detection, long-distance monitoring, and high sensitivity. It is especially suitable for mobile platforms of unmanned aerial vehicle and NanoSat. Can be widely used in trace gas detection, environmental pollution monitoring, medical diagnosis and military aircraft identification and guidance of anti-missile.
The simultaneous acquisition of spatial information, spectral information and polarimetric information can obtain more characteristic information to distinguish targets. The conventional spectral polarization imaging system mainly includes the filter/polarization wheel rotation system, the crystal modulation system and multi-path beam splitting system. The disadvantages of these systems are: unsynchronized spectral polarization detection, requiring dynamic modulation, complex system, etc. To solve these problems, a spectral polarization detection technology based on optical fiber image bundle is proposed, which combines optical fiber imaging spectral technology with pixel level polarization detection technology. The input shape of the optical fiber image bundle is plane, and the output shape is linear. Optical fiber image bundle can transform the information of array target into that of linear array. The linear array information is the input of spectral imaging system. The polarization detection uses a micron level polarization array to match the pixel size of the detector. The technology can synchronously acquire the two-dimensional spatial information, the spectral information and linear polarization information of the target. The technology can be used to image the area target in snapshot mode. The experimental device is set up to obtain the spectral image in the visible light range, as well as the polarization degree image and polarization angle image of each spectral segment. The data acquisition ability of the system is verified. With the improvement of optical fiber manufacturing technology, the integration of optical fiber is getting better, and the scale of optical fiber is getting larger. The technology will have a high application value in astronomical observation, atmospheric detection, target recognition and other fields.
The spectral polarization imager can detect the spectral polarization information of the target reflection or radiated light that cannot be obtained by ordinary optical instruments. The obtained spectral polarization image can provide richer target information than the intensity image and the spectral image. At the same time, being able to achieve snapshot imaging and improve the spectral resolution is the research and development direction of polarization spectrum imaging technology. In this paper, we present a dual channel snapshot compressive spectral polarization imaging technique for simultaneous acquisition of two-dimensional intensity information, one-dimensional spectral information, and four-dimensional polarization information of a target in visible range. One channel is based on a coded mask and micro-polarizer array, and one channel is based on a pixel-level polarizer array detector. The main optical path replaces the ordinary detector with a micro-polarizer array based on CASSI. The micro-polarizer array consists of 0°, 45°, 90°, and 135° linear micro-polarizers regularly distributed, and each pixel matches the pixel of the detector. The three Stokes parameters of the scene are compressed and sensed, and a four-dimensional (4D) data cube is projected onto a two-dimensional (2D) focal plane. Through nonlinear optimization with sparsity constraints, a 4D spectral polarization data cube is reconstructed from 2D measurements. The addition of a pixel-level polarizer array detector helps to improve the measurement accuracy of spectral information and polarization information. Optical experimental results confirm that the architecture reduces the total number of measurements required to obtain a spectrally polarized image compared to traditional acquisition methods. The dual channel combination enables simultaneous acquisition of spectral and polarization information, and improves the quality of reconstructed image based on compressed sensing algorithm. A dual-channel experimental device with coded aperture spectral polarization imaging channel and polarization imaging channel was set up to obtain spectral data cubes with 4 polarization states in 25 bands in the range of 450nm-650nm, and the polarization degree and polarization angle of each band. The spectral resolution was better than 10nm, and the spectral restoration accuracy was about 86.3%. Compared with the single-channel imaging method, the spectral reconstruction accuracy was improved by 10.5%.This has guiding significance for the design and research of light and miniaturized hyperspectral polarization imagers in the future. It is expected to be widely used in astronomical observation, atmospheric detection, biomedical diagnosis, earth environment monitoring, target detection and identification and other fields.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.