Recovering high dynamic range radiance maps from photographs
Proceedings of the 24th annual conference on Computer graphics and interactive techniques
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Gradient domain high dynamic range compression
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Fast bilateral filtering for the display of high-dynamic-range images
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
A Visibility Matching Tone Reproduction Operator for High Dynamic Range Scenes
IEEE Transactions on Visualization and Computer Graphics
Tone Reproduction for Realistic Images
IEEE Computer Graphics and Applications
Compressing and companding high dynamic range images with subband architectures
ACM SIGGRAPH 2005 Papers
Tone Mapping for HDR Image using Optimization A New Closed Form Solution
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
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Journal of Visual Communication and Image Representation
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IEEE Transactions on Pattern Analysis and Machine Intelligence
Edge-preserving decompositions for multi-scale tone and detail manipulation
ACM SIGGRAPH 2008 papers
Globally Optimized Linear Windowed Tone Mapping
IEEE Transactions on Visualization and Computer Graphics
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ACM SIGGRAPH 2011 papers
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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In this paper, we propose an effective locally nonlinear tone mapping algorithm for compressing the High Dynamic Range (HDR) images. Instead of linearly scaling the luminance of pixels, our core idea is to introduce local gamma correction with adaptive parameters on small overlapping patches over the entire input image. A framework for HDR image compression is then introduced, in which the global optimization problem is deduced and two guided images are adopted to induct the optimum solution. The optimal compression can finally be achieved by solving the optimization problem which can be transformed to a sparse linear equation. Extensive experimental results on a variety of HDR images and a carefully designed perceptually evaluation have demonstrated that our approach can achieve better performances than the state-of-the-art approaches.