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.

arXiv commentAAAI 2020 (Oral)

References in corpus (10)

• Adam: A Method for Stochastic Optimization
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Instance Normalization: The Missing Ingredient for Fast Stylization
• External Prior Guided Internal Prior Learning for Real-World Noisy Image Denoising
• Toward Convolutional Blind Denoising of Real Photographs
• Improving Color Constancy by Discounting the Variation of Camera Spectral Sensitivity
• Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset
• Learning to See in the Dark
• Deep Convolutional Denoising of Low-Light Images
• A Trilateral Weighted Sparse Coding Scheme for Real-World Image Denoising