As an efficient image contrast enhancement (CE) tool, adaptive gamma correction (AGC) was previously proposed by relating gamma parameter with cumulative distribution function (CDF) of the pixel gray levels within an image. ACG deals well with most dimmed images, but fails for globally bright images and the dimmed images with local bright regions. Such two categories of brightness-distorted images are universal in real scenarios, such as improper exposure and white object regions. In order to attenuate such deficiencies, here we propose an improved AGC algorithm. The novel strategy of negative images is used to realize CE of the bright images, and the gamma correction modulated by truncated CDF is employed to enhance the dimmed ones. As such, local over-enhancement and structure distortion can be alleviated. Both qualitative and quantitative experimental results show that our proposed method yields consistently good CE results.
翻译:作为一种高效的图像对比增强(CE)工具,适应性伽玛校正(AGC)以前是通过将伽玛参数与图像中像素灰度的累积分布功能(CDF)联系起来而提出的。 ACG处理的是大多数淡化图像,但未能满足全球亮度图像,也未能满足当地亮度图像的要求。在不适当的暴露和白色目标区域等真实情景中,这两类光度扭曲图像是普遍的。为了减轻这种缺陷,我们在此建议改进AGC算法。负值图像的新策略被用于实现亮度图像的CE,而由短径 CDF调节的伽马校正则被用于加强薄度图像。因此,可以缓解当地过度增强和结构扭曲的情况。在质量和数量两方面的实验结果都表明,我们拟议的方法产生了始终良好的 CE结果。