An Integrated Enhancement Solution for 24-hour Colorful Imaging

Abstract

The current industry practice for 24-hour outdoor imaging is to use a silicon camera supplemented with near-infrared (NIR) illumination. This will result in color images with poor contrast at daytime and absence of chrominance at nighttime. For this dilemma, all existing solutions try to capture RGB and NIR images separately. However, they need additional hardware support and suffer from various drawbacks, including short service life, high price, specific usage scenario, etc. In this paper, we propose a novel and integrated enhancement solution that produces clear color images, whether at abundant sunlight daytime or extremely low-light nighttime. Our key idea is to separate the VIS and NIR information from mixed signals, and enhance the VIS signal adaptively with the NIR signal as assistance. To this end, we build an optical system to collect a new VIS-NIR-MIX dataset and present a physically meaningful image processing algorithm based on CNN. Extensive experiments show outstanding results, which demonstrate the effectiveness of our solution.

An image fusion algorithm based on iterative wavelet transform for space non-cooperative targets

“…In Deng et al, 26 the CNN model cu-NET combined with sparse coding was proposed. In Lv et al, 27 the method is used to decouple RGB and NIR signals from the all-day monitoring picture. But these methods require large datasets and computers with powerful computing power.…”