基于统计特征分类耦合自适应 Gamma校正的图像增强算法

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。

Image enhancement based on image classification coupled adaptive Gamma correction

In order to avoid the color distortion caused by the brightness enhancement of the image and the problem of over-enhancementin the local area, an image enhancement algorithm based on the image classification coupled adaptive Gamma correction (AGC) wasdesigned to improve the image details and visual effects. Firstly, the input image was converted into HSV space and the color and thebrightness are separated, so that the original color of the pixel was not changed when the brightness channel was enhanced, and the colordistortion is effectively reduced. Secondly, considering the properties of different images, the images are classified into high and lowcontrast by using statistical information, and each contrast was divided into light and dark. Then, based on the traditional Gammacorrection method, an AGC was formed by dynamically setting parameters for different types of images, thus, different enhancementfunctions are constructed for different types of images to complete the enhancement of different types of images. The experimental datashow that compared with the current popular enhancement algorithms, the proposed algorithm has higher enhancement effect, whichpresents more natural brightness and contrast, as well as maintains more color information.