Deep Learning for Compressive Infrared and Hyperspectral Machine Vision

Recently there have been developed a host of approaches for more efficient hyperspectral imaging acquisition through hardware and algorithmic innovations. Unfortunately, these are all requiring the use of large, very-sensitive focal plane array detection and as such become prohibitively expensive. Infrared imaging similarly suffers from costly focal plane arrays to achieve high resolution images and therefore have not been employed for commercial machine vision applications. This proposal is a multifold approach to examine and overcome these obstacles and realize efficient means of both compressive infrared and hyperspectral computer recognition. Specifically, this project will address new compressive optical designs for infrared and spatio-spectral imaging and create the accompanying new deep learning algorithms for high-speed object detection/recognition directly from the compressive measurements. Our approach of a hardware-software hybrid will provide an increasingly detailed and systematic means for visually contextualizing objects and their surrounding environments in previously accessible but very costly portions of the electromagnetic spectrum.