陆 涛.基于统计特征分类耦合自适应 Gamma校正的图像增强算法[J].电子测量与仪器学报,2020,34(6):154-162
基于统计特征分类耦合自适应 Gamma校正的图像增强算法
Image enhancement based on image classification coupled adaptive Gamma correction
  
DOI:
中文关键词:  图像增强  统计特征分类  自适应 Gamma 校正  HSV 颜色空间  动态参数设置
英文关键词:image enhancement  image classification  adaptive Gamma correction  HSV color space  dynamic parameter setting
基金项目:广西省高校重点科研基金(KY2015Y13530)、广西高校科学技术研究项目(KY2015YB533)、广西邕宁区科技攻关项目(20160312A)资助
作者单位
陆 涛 1.南宁学院 信息工程学院 
AuthorInstitution
Lu Tao 1.School of Information Engineering, Nanning College 
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中文摘要:
      为了避免图像在亮度增强时导致其颜色失真,且在局部易出现过增强等问题,设计了统计特征分类耦合自适应 Gamma 校正(adaptive Gamma correction,AGC)的图像增强算法,以更好提高图像细节与视觉效果。 首先,将输入图像转换为 HSV 空间, 使颜色与亮度分离,使其在增强亮度通道时不改变像素的原始颜色,有效降低颜色失真。 然后,考虑不同图像的性质,利用统计 信息将图像分类为高、低两种对比度,每种对比度又分为亮、暗两类。 其次,基于传统的 Gamma 校正方法,通过对于不同类型的 图像进行动态参数设置,形成一种 AGC 机制,从而为不同类型图像的构建了不同的增强函数,以完成不同类别图像的增强处 理。 实验数据表明,与当前流行的增强算法相比,所提算法具备更高的增强效果,呈现出更为自然的亮度与对比度,且保持了更 多的颜色信息。
英文摘要:
      In order to avoid the color distortion caused by the brightness enhancement of the image and the problem of over-enhancement in the local area, an image enhancement algorithm based on the image classification coupled adaptive Gamma correction (AGC) was designed to improve the image details and visual effects. Firstly, the input image was converted into HSV space and the color and the brightness are separated, so that the original color of the pixel was not changed when the brightness channel was enhanced, and the color distortion is effectively reduced. Secondly, considering the properties of different images, the images are classified into high and low contrast by using statistical information, and each contrast was divided into light and dark. Then, based on the traditional Gamma correction method, an AGC was formed by dynamically setting parameters for different types of images, thus, different enhancement functions are constructed for different types of images to complete the enhancement of different types of images. The experimental data show that compared with the current popular enhancement algorithms, the proposed algorithm has higher enhancement effect, which presents more natural brightness and contrast, as well as maintains more color information.
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