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Objective Color reproduction plays a very important role in textile, printing, telemedicine, and other industries, but affected by the manufacturing process or color rendering mechanism of digital image acquisition equipment, color image transmission between digital devices often has color distortion. Meanwhile, once the distortion appears, the abovementioned industries will suffer losses or even irreversible damage. During color image acquisition, the most commonly employed acquisition equipment is the digital camera, which is an important method to convert the color image collected by the digital camera into the image seen by the human eye (or the camera characteristic method). Although the existing nonlinear camera characterization methods have the best camera characterization performance at present, these methods have hue distortion. To retain the important properties of the hueplane preserving and further improve the camera characterization performance, we propose a huesubregion weighted constrained hueplane preserving camera characterization (HPPCC -NWCM) method.Methods The proposed method improves weighted constrained hueplane preserving camera characterization from the perspective of optimizing the huesubregion. First, the camera response value RGBs and the colorimetric value XYZs of the training samples are synchronously preprocessed, with the hue angles calculated and hue subregions preliminarily divided. Then, by operating in the hue subregion, the minimum hue angle differences between each training sample and the samples in the hue subregion are employed as the weighted power function, and the precalculation camera characterization matrices (precalculation matrices) are calculated for each sample respectively. Additionally, the weighted constrained normalized camera characterization matrix in the hue subregion is obtained by weighted averaging of the precalculation matrices using the weighted power function. Combined with the characterization results of samples within the hue subregion and all samples, the number and position of the hue subregions are optimized, and those under the best performance are obtained. To verify the performance improvement of this method, we conduct simulation experiments. Firstly, the huesubregion number selection experiment is carried out by combining three cameras and three groups of object reflectance datasets under the D65 light illuminant. Then, the two cameras from the previous experimental data are compared with existing methods for further experiments and the exposure independence of each method is verified by 0933001 -11 研究论文 第 44 卷 第 9 期/2024 年 5 月/光学学报 changing the exposure level. Finally, the SFU dataset is compared with the existing methods repeatedly with 42 cameras under three light illuminants.Results and Discussions Verified by many simulation experiments and real camera experiments, in the simulation experiment of selecting the huesubregion number, the camera characterization performance of this method is generally enhanced with the increasing huesubregion...
Objective Color reproduction plays a very important role in textile, printing, telemedicine, and other industries, but affected by the manufacturing process or color rendering mechanism of digital image acquisition equipment, color image transmission between digital devices often has color distortion. Meanwhile, once the distortion appears, the abovementioned industries will suffer losses or even irreversible damage. During color image acquisition, the most commonly employed acquisition equipment is the digital camera, which is an important method to convert the color image collected by the digital camera into the image seen by the human eye (or the camera characteristic method). Although the existing nonlinear camera characterization methods have the best camera characterization performance at present, these methods have hue distortion. To retain the important properties of the hueplane preserving and further improve the camera characterization performance, we propose a huesubregion weighted constrained hueplane preserving camera characterization (HPPCC -NWCM) method.Methods The proposed method improves weighted constrained hueplane preserving camera characterization from the perspective of optimizing the huesubregion. First, the camera response value RGBs and the colorimetric value XYZs of the training samples are synchronously preprocessed, with the hue angles calculated and hue subregions preliminarily divided. Then, by operating in the hue subregion, the minimum hue angle differences between each training sample and the samples in the hue subregion are employed as the weighted power function, and the precalculation camera characterization matrices (precalculation matrices) are calculated for each sample respectively. Additionally, the weighted constrained normalized camera characterization matrix in the hue subregion is obtained by weighted averaging of the precalculation matrices using the weighted power function. Combined with the characterization results of samples within the hue subregion and all samples, the number and position of the hue subregions are optimized, and those under the best performance are obtained. To verify the performance improvement of this method, we conduct simulation experiments. Firstly, the huesubregion number selection experiment is carried out by combining three cameras and three groups of object reflectance datasets under the D65 light illuminant. Then, the two cameras from the previous experimental data are compared with existing methods for further experiments and the exposure independence of each method is verified by 0933001 -11 研究论文 第 44 卷 第 9 期/2024 年 5 月/光学学报 changing the exposure level. Finally, the SFU dataset is compared with the existing methods repeatedly with 42 cameras under three light illuminants.Results and Discussions Verified by many simulation experiments and real camera experiments, in the simulation experiment of selecting the huesubregion number, the camera characterization performance of this method is generally enhanced with the increasing huesubregion...
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