Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
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LCIS: a boundary hierarchy for detail-preserving contrast reduction
Proceedings of the 26th annual conference on Computer graphics and interactive techniques
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Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
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ACM SIGGRAPH 2003 Papers
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ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
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ACM SIGGRAPH 2006 Papers
ACM SIGGRAPH 2006 Papers
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ACM SIGGRAPH 2007 papers
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ACM SIGGRAPH 2007 papers
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ACM SIGGRAPH 2008 papers
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HPCC'07 Proceedings of the Third international conference on High Performance Computing and Communications
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This paper presents a GPU-based implementation for constructing edge-preserving multiscale image decompositions. An input image is decomposed into a piecewise smooth base layer and multiple detail layers. The base layer captures large scale variations in the image, while the detail layers contain the small scale details. The detail layers are progressively obtained with the edge-preserving weighted least squares optimizations. The improvement of performance is achieved by introducing a Jacobi-like GPU solver, which converges to the right solution much faster than the standard Jacobi iterator. Note that the whole pipeline design is highly parallel, enabling a real-time implementation. Several experimental examples on edge-preserving tonal adjustment and image abstraction are shown to demonstrate the feasibility of the proposed method.