The computed tomography imaging spectrometer (CTIS) is a snapshot capable hyperspectral camera. A diffractive optical element is used to create multiple projections of the hyperspectral data cube side by side on the image sensor. A reconstruction algorithm computes the hyperspectral image from the spatio-spectral multiplexed signal. It solves a similar problem as the reconstruction algorithms used for computed tomography scanners. We present how such a system can be realized by a parallelized approach. Several apertures are placed next to each other. Each aperture creates only one projection using a grating prism.
The Computed Tomography Imaging Spectrometer (CTIS) is a snapshot hyperspectral camera based on computational imaging. Typical designs use a Keplerian beam expander to limit the imaged scene and decrease the incident ray angles. We have found that the use of a Galilean beam expander instead can be beneficial. Often a smaller system with a better optical quality is achievable with the disadvantage of a vignetted image. Results of a comparison between both designs based on a prototype will be shown. Furthermore, we present our miniaturized CTIS system, which uses the Galilean design.
The computed tomography imaging spectrometer (CTIS) is a hyperspectral imaging (HSI) approach where spectral and spatial information of a scene is mixed during the imaging process onto a monochromatic sensor. This mixing is due to a diffractive optical element integrated into the underlying optics and creates a set of diffraction orders. To reconstruct a three-dimensional hyperspectral cube from the CTIS sensor image, iterative algorithms were applied. Unfortunately, such methods are highly sensitive to noise and require high computational time for reconstruction thus hindering their applicability in real-time and high frame-rate applications. To overcome such limitations, we propose a lightweight and efficient deep convolutional neural network for hyperspectral image reconstruction from CTIS sensor images. Compared with classical approaches our model delivers considerably better reconstruction results on synthetic as well as real CTIS images in under 0.17 s, which is over 60 times faster compared with the standard iterative approach. In addition, the reshaping method we have developed enables a lightweight network architecture with over 100 times fewer parameters than previously reported.
In this contribution, an approach for the characterization of various fiber-based slit homogenizer devices in the NIR and SWIR is shown. The devices are to be tested for use in a satellite-based spectrometer for spatial monitoring of anthropogenic greenhouse gases. This leads to the characterization requirement for temporal coherence and spatial incoherence. Speckle noise has to be reduced to a very low level, which is achieved using a fixed diffusor in combination with a rotating diffusor and a tunable (wavelength) laser as well as temporal averaging. Remaining variations due to unwanted interferences at the imager are removed by controlled movement of the sensor with an automated micro positioning stage in combination with image processing. The design, realization and characterization of the measurement breadboard as well as near field homogenization results for different input scenes and polarizations are shown. Additionally, the geometric characteristics and the depolarization effect of the fibers are investigated for a homogeneous input scene. Furthermore, a setup and measurement results concerning the focal ratio degradation of the fibers are presented.
As streets occupy a growing proportion of our world, they possess great potential as reference for optical, atmospheric research. Moreover, the demand for infrared sensors which distinguish asphalt from other grounds increases due to automation in agriculture, and more generally the introduction of highly autonomous vehicles. Thus, it is favorable to investigate and especially understand the optical properties of asphalt in the nearinfrared (NIR). In our study, we investigate the infrared reflectance of more than 15 asphalt types with different measurement geometries. As a prerequisite, we analyze the dependence of the diffuse reflectance factor on the light spot size, in order to determine a minimum light spot size for any further optical study. By comparing the BRF (bidirectional reflectance factor) to a nearly-Lambertian Spectralon white standard, we show that asphalt is in general a good diffuse scatterer. While the absolute value of the near-infrared reflectance factor varies dependent on asphalt type and wear, its slope shows typically only slight variations; nevertheless, one asphalt differs significantly from the others. For a more detailed analysis, pieces of this and other asphalts were pulverized and pressed into pellets, suitable for broadband infrared spectroscopy. With this, we are able to identify a feature between 460 nm and 6300 nm, whose intensity determines the slope of the NIR response. Here, we discuss its dependence on the asphalt’s ingredients, as well as the contribution of the grain size structure to the actual reflectance of real asphalt.
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