Abstract
Emerging sensor designs increasingly rely on novel color filter arrays (CFAs) to sample the incident spectrum in unconventional ways. In particular, capturing a near-infrared (NIR) channel along with conventional RGB color is an exciting new imaging modality. RGB+NIR sensing has broad applications in computational photography, such as low-light denoising, it has applications in computer vision, such as facial recognition and tracking, and it paves the way toward low-cost single-sensor RGB and depth imaging using structured illumination. However, cost-effective commercial CFAs suffer from severe spectral cross talk. This cross talk represents a major challenge in high-quality RGB+NIR imaging, rendering existing spatially multiplexed sensor designs impractical. In this work, we introduce a new approach to RGB+NIR image reconstruction using learned convolutional sparse priors. We demonstrate high-quality color and NIR imaging for challenging scenes, even including high-frequency structured NIR illumination. The effectiveness of the proposed method is validated on a large data set of experimental captures, and simulated benchmark results which demonstrate that this work achieves unprecedented reconstruction quality.
Original language | English (US) |
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Article number | 8170267 |
Pages (from-to) | 1611-1625 |
Number of pages | 15 |
Journal | IEEE Transactions on Image Processing |
Volume | 27 |
Issue number | 4 |
DOIs | |
State | Published - Apr 2018 |
Externally published | Yes |
All Science Journal Classification (ASJC) codes
- Software
- Computer Graphics and Computer-Aided Design
Keywords
- Computational photography
- convolutional sparse coding
- structured illumination