论文标题
面向反射率的概率均衡以增强图像
Reflectance-Oriented Probabilistic Equalization for Image Enhancement
论文作者
论文摘要
尽管图像增强了最新的进步，但现有方法仍然很难适应弱光和正常光图像的亮度和对比度。为了解决这个问题，我们提出了一种新型的2D直方直方图均衡方法。它假设强度发生和同时存在相互依赖，并通过在强度共发生的分布（2D直方图）上进行边缘化，从而导致强度发生的分布（1D直方图）。该方案更有效地改善了全局对比度，并减少了噪声扩增。 2D直方图是通过将图像反射率中的局部像素值差异纳入密度估计以减轻暗照明条件的不利影响的定义。超过500张图像用于评估，证明了我们的方法优于现有研究。它可以充分提高低光图像的亮度，同时避免在正常光线图像中过度增强。
Despite recent advances in image enhancement, it remains difficult for existing approaches to adaptively improve the brightness and contrast for both low-light and normal-light images. To solve this problem, we propose a novel 2D histogram equalization approach. It assumes intensity occurrence and co-occurrence to be dependent on each other and derives the distribution of intensity occurrence (1D histogram) by marginalizing over the distribution of intensity co-occurrence (2D histogram). This scheme improves global contrast more effectively and reduces noise amplification. The 2D histogram is defined by incorporating the local pixel value differences in image reflectance into the density estimation to alleviate the adverse effects of dark lighting conditions. Over 500 images were used for evaluation, demonstrating the superiority of our approach over existing studies. It can sufficiently improve the brightness of low-light images while avoiding over-enhancement in normal-light images.
