Generalized Inpainting Method for Hyperspectral Image Acquisition

A recently designed hyperspectral imaging device enables multiplexed acquisition of an entire data volume in a single snapshot thanks to monolithically-integrated spectral filters. Such an agile imaging technique comes at the cost of a reduced spatial resolution and the need for a demosaicing procedure on its interleaved data. In this work, we address both issues and propose an approach inspired by recent developments in compressed sensing and analysis sparse models. We formulate our superresolution and demosaicing task as a 3-D generalized inpainting problem. Interestingly, the target spatial resolution can be adjusted for mitigating the compression level of our sensing. The reconstruction procedure uses a fast greedy method called Pseudo-inverse IHT. We also show on simulations that a random arrangement of the spectral filters on the sensor is preferable to regular mosaic layout as it improves the quality of the reconstruction. The efficiency of our technique is demonstrated through numerical experiments on both synthetic and real data as acquired by the snapshot imager.Comment: Keywords: Hyperspectral, inpainting, iterative hard thresholding, sparse models, CMOS, Fabry-P\'ero

On-Orbit Results from an Ultra-Low SWaP Black Silicon Star Tracker

In August 2019, two 1.5U AeroCube-10 satellites built by The Aerospace Corporation were deployed from a Cygnus resupply spacecraft. Each of the satellites has two star trackers which are many times smaller than commercially available alternatives. The significant size reduction is enabled by the SiOnyx XQE-0920 sensor which offers dramatically improved visible and near-infrared sensitivity in an uncooled CMOS platform. This allows the use of a smaller-aperture lens than traditionally used in small form factor star trackers, while maintaining the ability to detect stars of magnitude 5. The reduced volume enables innovative system engineering trades such as forgoing star tracker baffles, and instead flying multiple sensors on the same spacecraft to combat stray light by using the spacecraft body itself as a shield. The additional interior volume made available also enables more capable missions in smaller CubeSat form factors. On-orbit results are presented showing angular accuracy and solution availability statistics as a function of angular rotation rate. A calibration technique to compensate for optical distortion is also presented, which enables the use of a low-cost COTS lens with a wide field of view. Despite the extremely small volume, the star tracking performance is comparable to units many times larger

Hierarchical stack filtering : a bitplane-based algorithm for massively parallel processors

With the development of novel parallel architectures for image processing, the implementation of well-known image operators needs to be reformulated to take advantage of the so-called massive parallelism. In this work, we propose a general algorithm that implements a large class of nonlinear filters, called stack filters, with a 2D-array processor. The proposed method consists of decomposing an image into bitplanes with the bitwise decomposition, and then process every bitplane hierarchically. The filtered image is reconstructed by simply stacking the filtered bitplanes according to their order of significance. Owing to its hierarchical structure, our algorithm allows us to trade-off between image quality and processing time, and to significantly reduce the computation time of low-entropy images. Also, experimental tests show that the processing time of our method is substantially lower than that of classical methods when using large structuring elements. All these features are of interest to a variety of real-time applications based on morphological operations such as video segmentation and video enhancement