Image and depth from a conventional camera with a coded aperture
ACM SIGGRAPH 2007 papers
ACM SIGGRAPH 2009 papers
User-assisted intrinsic images
ACM SIGGRAPH Asia 2009 papers
Image statistics: from data collection to applications in graphics
ACM SIGGRAPH 2010 Courses
Separating reflections from a single image using spatial smoothness and structure information
LVA/ICA'10 Proceedings of the 9th international conference on Latent variable analysis and signal separation
Visual enhancement of old documents with hyperspectral imaging
Pattern Recognition
Illumination decomposition for material recoloring with consistent interreflections
ACM SIGGRAPH 2011 papers
Resolving permutation ambiguity in correlation-based blind image separation
Pattern Recognition Letters
Matting and Compositing for Fresnel Reflection on Wavy Surfaces
Computer Graphics Forum
Smoke Detection in Video: An Image Separation Approach
International Journal of Computer Vision
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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.