Color image enhancement via chromaticity diffusion
A novel approach for color image denoising is proposed in this paper. The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color p...
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| Veröffentlicht in: | IEEE transactions on image processing 2001-05, Vol.10 (5), p.701-707 |
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| creator | Tang, B. Sapiro, G. Caselles, V. |
| description | A novel approach for color image denoising is proposed in this paper. The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color pixel is considered as an n-dimensional vector. The vectors' direction, a unit vector, gives the chromaticity, while the magnitude represents the pixel brightness. The chromaticity is processed with a system of coupled diffusion equations adapted from the theory of harmonic maps in liquid crystals. This theory deals with the regularization of vectorial data, while satisfying the intrinsic unit norm constraint of directional data such as chromaticity. Both isotropic and anisotropic diffusion flows are presented for this n-dimensional chromaticity diffusion flow. The brightness is processed by a scalar median filter or any of the popular and well established anisotropic diffusion flows for scalar image enhancement. We present the underlying theory, a number of examples, and briefly compare with the current literature. |
| doi_str_mv | 10.1109/83.918563 |
| format | Article |
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The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color pixel is considered as an n-dimensional vector. The vectors' direction, a unit vector, gives the chromaticity, while the magnitude represents the pixel brightness. The chromaticity is processed with a system of coupled diffusion equations adapted from the theory of harmonic maps in liquid crystals. This theory deals with the regularization of vectorial data, while satisfying the intrinsic unit norm constraint of directional data such as chromaticity. Both isotropic and anisotropic diffusion flows are presented for this n-dimensional chromaticity diffusion flow. The brightness is processed by a scalar median filter or any of the popular and well established anisotropic diffusion flows for scalar image enhancement. 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The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color pixel is considered as an n-dimensional vector. The vectors' direction, a unit vector, gives the chromaticity, while the magnitude represents the pixel brightness. The chromaticity is processed with a system of coupled diffusion equations adapted from the theory of harmonic maps in liquid crystals. This theory deals with the regularization of vectorial data, while satisfying the intrinsic unit norm constraint of directional data such as chromaticity. Both isotropic and anisotropic diffusion flows are presented for this n-dimensional chromaticity diffusion flow. The brightness is processed by a scalar median filter or any of the popular and well established anisotropic diffusion flows for scalar image enhancement. We present the underlying theory, a number of examples, and briefly compare with the current literature.</description><subject>Algorithms</subject><subject>Anisotropic magnetoresistance</subject><subject>Anisotropy</subject><subject>Applied sciences</subject><subject>Brightness</subject><subject>Chromaticity</subject><subject>Color</subject><subject>Diffusion</subject><subject>Engineering profession</subject><subject>Exact sciences and technology</subject><subject>Image processing</subject><subject>Information, signal and communications theory</subject><subject>Liquid crystals</subject><subject>Mathematical analysis</subject><subject>Noise reduction</subject><subject>Partial differential equations</subject><subject>Power harmonic filters</subject><subject>Signal processing</subject><subject>Smoothing methods</subject><subject>Studies</subject><subject>Telecommunications and information theory</subject><subject>Vectors (mathematics)</subject><issn>1057-7149</issn><issn>1941-0042</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2001</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNqN0UtLAzEQB_Agiq3Vg1cPsigoHrYmm_dRii8oeOl9SbOJTdlHTXaFfntTdrHgQT0lJD9mhvkDcI7gFCEo7wWeSiQowwdgjCRBKYQkO4x3SHnKEZEjcBLCGkJEKGLHYIRERiRjcAyyWVM2PnGVejeJqVeq1qYydZt8OpXolW8q1Trt2m1SOGu74Jr6FBxZVQZzNpwTsHh6XMxe0vnb8-vsYZ5qQmGbMkgpo9xYISQkHBONuWBU60wZpVVBeXyQFscZJVxayy1bUoWkkFYWEuMJuO3Lbnzz0ZnQ5pUL2pSlqk3ThVwiwigklPwpY28kGIE0yptfZSYixOQfkDEqCZMRXv2A66bzddxLLgRhGcvIbsC7HmnfhOCNzTc-btxvcwTzXYK5wHmfYLSXQ8FuWZliL4fIIrgegApaldbHyFz4dpJSLnhUF71yxpj9Z9_jC6UAp0E</recordid><startdate>20010501</startdate><enddate>20010501</enddate><creator>Tang, B.</creator><creator>Sapiro, G.</creator><creator>Caselles, V.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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The algorithm is based on separating the color data into chromaticity and brightness, and then processing each one of these components with partial differential equations or diffusion flows. In the proposed algorithm, each color pixel is considered as an n-dimensional vector. The vectors' direction, a unit vector, gives the chromaticity, while the magnitude represents the pixel brightness. The chromaticity is processed with a system of coupled diffusion equations adapted from the theory of harmonic maps in liquid crystals. This theory deals with the regularization of vectorial data, while satisfying the intrinsic unit norm constraint of directional data such as chromaticity. Both isotropic and anisotropic diffusion flows are presented for this n-dimensional chromaticity diffusion flow. The brightness is processed by a scalar median filter or any of the popular and well established anisotropic diffusion flows for scalar image enhancement. 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| subjects | Algorithms Anisotropic magnetoresistance Anisotropy Applied sciences Brightness Chromaticity Color Diffusion Engineering profession Exact sciences and technology Image processing Information, signal and communications theory Liquid crystals Mathematical analysis Noise reduction Partial differential equations Power harmonic filters Signal processing Smoothing methods Studies Telecommunications and information theory Vectors (mathematics) |
| title | Color image enhancement via chromaticity diffusion |
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