计算机科学
光学(聚焦)
人工智能
图像(数学)
叠加原理
高斯分布
计算机视觉
模式识别(心理学)
数学
量子力学
光学
物理
数学分析
标识
DOI:10.1109/tpami.2007.1106
摘要
When we take a picture through transparent glass, the image we obtain is often a linear superposition of two images: The image of the scene beyond the glass plus the image of the scene reflected by the glass. Decomposing the single input image into two images is a massively ill-posed problem: In the absence of additional knowledge about the scene being viewed, there are an infinite number of valid decompositions. In this paper, we focus on an easier problem: user assisted separation in which the user interactively labels a small number of gradients as belonging to one of the layers. Even given labels on part of the gradients, the problem is still ill-posed and additional prior knowledge is needed. Following recent results on the statistics of natural images, we use a sparsity prior over derivative filters. This sparsity prior is optimized using the iterative reweighted least squares (IRLS) approach. Our results show that using a prior derived from the statistics of natural images gives a far superior performance compared to a Gaussian prior and it enables good separations from a modest number of labeled gradients.
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