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- W2310362711 abstract "Acquiring high quality imagery in darkness has been a challenging problem in computer vision. In this dissertation, we propose to develop a new imaging system that is suitable for low light conditions. Our proposed solution is to integrate multispectral, multi-speed, multi-resolution, and multi-focus sensors into a hybrid camera array. We also develop companion computational photography algorithms for effectively fusing the captured imagery data. On the system side, I extend the University of Delaware (UD) light field camera array by integrating a broader range of cameras. Specifically, our proposed system will consist of a pair of high-resolution monochrome (HR-M) cameras, a pair of high-speed monochrome (HS-M) cameras, and a single high-resolution RGB color (HR-C) camera. The HR-M cameras further use a wide aperture for acquiring images with low level of noise images whereas the HS-M cameras use fast shutters for capturing motion-blur free images on fast moving objects. Finally, the HR-C camera is used to capture color information about the scene and it uses slow shutters to reduce the color noise. On the algorithm side, I develop a class of novel algorithms that combine the capabilities of classical computer vision and computational photography for fusing the imagery data from the hybrid sensors. First, I propose to develop techniques for fusing the images from the HR-M cameras. Since each HR-M camera uses a wide aperture, its images exhibit shallow depth-of-field effects, i.e., it can only focus at one depth layer of the scene. Therefore, we focus the two HR-M cameras at different scene depth and design multi-focus multi-view fusing methods for synthesizing all-focus video streams. Next, I use the HR-M imagery data as the multispectral prior to denoise the HS-M streams. We first preprocess the HS-M images to improve their contrast using a virtual exposure fusion technique. We then locate the corresponding patches on the HR-M images. Finally, to denoise the HS-M image, I design an alternating optimization algorithm by extending the Total Variation (TV) denoising algorithm by imposing the gradient fields of the corresponding HR-M patches as priors. Recall that the HR-C camera uses a slow shutter and therefore will incur severe motion blurs on moving targets. To resolve this problem, I use the denoised HS-M frames for estimating the blur kernel on the HR-C camera and then apply deblurring techniques to reduce/remove motion blurs. We have applied our hybrid camera array system to capture both indoor and outdoor scenes under low light. Experimental results show that our solution can greatly enhance the quality of the imagery by reducing noise, improving image sharpness, and removing motion blurs." @default.
- W2310362711 created "2016-06-24" @default.
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- W2310362711 date "2011-01-01" @default.
- W2310362711 modified "2023-09-25" @default.
- W2310362711 title "A hybrid camera system for low-light imaging" @default.
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