Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smart Interpolation by Anisotropic Diffusion
ICIAP '03 Proceedings of the 12th International Conference on Image Analysis and Processing
Image Compression with Anisotropic Diffusion
Journal of Mathematical Imaging and Vision
Colour Gamut Mapping as a Constrained Variational Problem
SCIA '09 Proceedings of the 16th Scandinavian Conference on Image Analysis
Space-dependent color gamut mapping: a variational approach
IEEE Transactions on Image Processing
Retaining Local Image Information in Gamut Mapping Algorithms
IEEE Transactions on Image Processing
A Multiscale Framework for Spatial Gamut Mapping
IEEE Transactions on Image Processing
Image-Dependent Gamut Mapping as Optimization Problem
IEEE Transactions on Image Processing
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We present a computationally efficient, artifact-free, spatial colour gamut mapping algorithm. The proposed algorithm offers a compromise between the colorimetrically optimal gamut clipping and an ideal spatial gamut mapping. It exploits anisotropic diffusion to reduce the introduction of halos often appearing in spatially gamut mapped images. It is implemented as an iterative method. At iteration level zero, the result is identical to gamut clipping. The more we iterate the more we approach an optimal, spatial gamut mapping result. Our results show that a low number of iterations, 10-20, is sufficient to produce an output that is as good or better than that achieved in previous, computationally more expensive, methods. The computational complexity for one iteration is O(N), N being the number of pixels. Results based on a challenging small destination gamut supports our claims that it is indeed efficient.