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Robust PCA-based solution to image composition using augmented Lagrange multiplier (ALM)
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DOI：10.1007/s00371-015-1075-1.png)
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摘要
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Computational photography relies on specialized image-processing techniques to combine multiple images captured by a camera to generate a desired image of the scene. We first consider the high dynamic range (HDR) imaging problem. We can change either the exposure time or the aperture while capturing multiple images of the scene to generate an HDR image. This paper addresses the HDR imaging problem for static and dynamic scenes captured using a stationary camera under various aperture and exposure settings, when we do not have any knowledge of the camera settings. We have proposed a novel framework based on sparse representation which enables us to process images while getting rid of artifacts due to moving objects and defocus blur. We show that the proposed approach is able to produce significantly good results through dynamic object rejection and deblurring capabilities. We compare the results with other competitive approaches and discuss the relative advantages of the proposed approach.
Keyword:
Robust principal component analysis
HDR imaging
Variable aperture photography
Dynamic scene
Deblurring
Deghosting
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