| 祝振敏,吕兆康,刘百芬.基于LS-SVR的图像矫正[J].,2016,56(1):86-91 |
| 基于LS-SVR的图像矫正 |
| Image correction based on LS-SVR |
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| DOI：10.7511/dllgxb201601013 |
| 中文关键词:颜色空间最小二乘支持向量回归(LS-SVR)图像矫正色差 |
| 英文关键词:color spacesthe least squares support vector regression (LS-SVR)image correctionchromatic aberration |
| 基金项目:国家自然科学基金资助项目(51305137)；江西省科技支撑计划资助项目(20151BBE50116)；江西省教育厅自然科学基金资助项目(GJJ14388). |
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| 全文下载次数:1475 |
| 中文摘要: |
| 最小二乘支持向量回归(the least squares support vector regression，LS-SVR)算法因其回归拟合度高广泛应用于各领域中．以目标物在不同光源下采集的图像呈现出不同的颜色值，从而导致图像与目标物出现视觉上的偏差为研究对象，并以潘通色卡为参照，利用LS-SVR算法，结合将RGB颜色空间到sRGB颜色空间的转换模型，对测试图像进行矫正处理．实验结果表明 与多项式回归相比，LS-SVR算法能取得更小的色差，且矫正后的图像更接近于目标图像． |
| 英文摘要: |
| The least squares support vector regression (LS-SVR) algorithm is widely applied to diverse research fields due to the advantage of higher fitting degree. Various color values are acquired from images gathered under diverse illuminants, which cause the deviation between the images and the objective. Pantone cards are regarded as reference and LS-SVR algorithm is employed to process image correction via the transformation model from RGB to sRGB color space. As illustrated in the experimental results, a better performance for the lower value of chromatic aberration and highly approximating to the object image after image correction is obtained by LS-SVR algorithm compared with polynomial regression. |
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