Deep-learning blurring correction of images obtained from NIR single-pixel imaging.

Abstract

In challenging scenarios characterized by low-photon conditions or the presence of scattering effects caused by rain, fog, or smoke, conventional silicon-based cameras face limitations in capturing visible images. This often leads to reduced visibility and image contrast. However, using near-infrared (NIR) light within the range of 850-1550nm offers the advantage of reduced scattering by microparticles, making it an attractive option for imaging in such conditions. Despite NIR's advantages, NIR cameras can be prohibitively expensive. To address this issue, we propose a vision system that leverages NIR active illumination single-pixel imaging (SPI) operating at 1550nm combined with time of flight operating at 850nm for 2D image reconstruction, specifically targeting rainy conditions. We incorporate diffusion models into the proposed system to enhance the quality of NIR-SPI images. By simulating various conditions of background illumination and droplet size in an outdoor laboratory scenario, we assess the feasibility of utilizing NIR-SPI as a vision sensor in challenging outdoor environments.