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Title: Computational low-light flash photography
Keywords: Image deblurring, Image denoising, Near Infrared, Image enhancement, Computational photography, Low-light photography
Issue Date: 9-Jan-2011
Citation: ZHUO SHAOJIE (2011-01-09). Computational low-light flash photography. ScholarBank@NUS Repository.
Abstract: While the performance of modern digital cameras have been improved remarkably, taking photographs under low-light conditions is still challenging. Photographs taken with optimal camera settings may be corrupted by noise or blur. Researchers across disciplines has studied photograph enhancement under low-light conditions for decades. In light of previous studies, this thesis proposes Computational Low-Light Flash Photography. We exploit the correlation between no-flash and flash photographs of the same scene to produce high quality photographs under low-light conditions. We propose a novel image deblurring method by using a pair of motion blurred and flash images taken using a conventional camera. We investigate the correlation between the sharp image and its corresponding flash image and use it to constrain the image deblurring. We show that our method is able to estimate an accurate blur kernel and reconstruct a high-quality sharp image and outperforms existing deblurring methods. In situations that a normal visible flash cannot be used, we propose to use a near infrared (NIR) flash and build a hybrid camera system to take a noisy visible image and its NIR counterpart simultaneously. We then present a novel image smoothing and fusion method that combines the image pair to generate a cleaner image with enhanced details. Intensive experimental results demonstrate that our approach outperforms the state-of-the-art image denoising methods. The methods proposed in this thesis provide a practical and effective way for high-quality low-light photography. Moreover, our work enables better understanding of the correlation between flash and no-flash images in both visible and NIR spectrum and thus provides more insights for image enhancement using correlated images.
Appears in Collections:Ph.D Theses (Open)

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