FACE: Fully Automated Context Enhancement for night-time video sequences

Anwaar Ul-Haq, Xiaoxia Yin, Jing He, Yanchun Zhang

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)


Unfavorable lighting conditions pose significant challenges to imaging devices. Use of multiple sensor fusion and false colorization has been demonstrated by many recent works. However, semi-automatic nature of these approaches make them less attractive. To address these shortcomings, we present a color night vision system, named FACE (Fully Automated Context Enhancement). It uses enhanced multi-spectral video fusion and fully automated color transform based color morphing to process multiple video streams captured by infrared (IR) and low light visible (VIS) sensors. At first, we introduce glare-suppressed inter-channel fusion in RGB color space to negate the ghost-like effect produced by IR channel. We then introduce color value imputation with deep KNN framework for joint classification of grayscale and color imagery. A suitable color transform is then sought to give a day-like color appearance to night vision imagery. Objective quality evaluation indicates the effectiveness of our framework for context enhancement at nighttime.
Original languageEnglish
Pages (from-to)682-693
Number of pages12
JournalJournal of Visual Communication and Image Representation
Issue numberB
Publication statusPublished - 2016


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