Optimum Parameter Estimation of Tone Mapping Operators by Natural Image Statistics
| Autor: | Daiki Okazaki, Kohei Inoue, Kiichi Urahama, Kenji Hara |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Estimation theory natural image statistics ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION high dynamic range image Pattern recognition Probability density function Tone mapping Wavelet Operator (computer programming) Computer Science::Computer Vision and Pattern Recognition Histogram Prior probability Artificial intelligence tone mapping operator Divergence (statistics) business ComputingMethodologies_COMPUTERGRAPHICS |
| Zdroj: | Journal of the Institute of Industrial Applications Engineers. 7:106-109 |
| ISSN: | 2187-8811 2188-1758 |
| DOI: | 10.12792/jiiae.7.106 |
| Popis: | In this paper, we propose a method that optimizes the parameters of tone mapping operators by compressing the dynamic range of HDR images using natural image statistics.First, a prior probability model of a natural image is constructed for color natural images based on a generalized Gaussian distribution.Then, an LDR image is generated by converting the HDR image using the tone mapping operator. Next, we generate a normalized histogram of the LDR image using a discrete wavelet transformation.Finally, the optimal parameters of the tone mapping operator are estimated by minimizing the Kullbuck--Leibler divergence of the probability density function and the normalized histogram.Using these parameters, it is possible to generate an LDR image that closely resembles the natural image. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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