In this paper, we propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and gray-level selective down-sampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is novelly proposed to reconstruct the pixel mapping on the gray levels missed by downsampling. As two case studies, accelerations of histogram equalization (HE) and the state-of-the-art global CE algorithm, i.e., spatial mutual information and PageRank (SMIRANK), are presented detailedly. Both quantitative and qualitative assessment results have verified the effectiveness of our proposed CE acceleration framework. In typical tests, computational efficiencies of HE and SMIRANK have been speeded up by about 3.9 and 13.5 times, respectively.

翻译：在本文中,我们提出了一个加速普遍直方图图像对比增强(CE)算法的总体框架。对数字图像采用空间和灰层的选择性下取样法,以降低计算成本,而增强图像的视觉质量仍得到保存,且没有明显退化。绘图功能校准是新颖的提议,以重建在下标所遗漏的灰色水平上的像素绘图。作为两个案例研究,将加速直方图均匀(HE)和最先进的全球CE算法,即空间相互信息和PageRank(SMIRANK)详细介绍。定量和定性评估结果都验证了我们提议的CE加速框架的有效性。在典型的测试中,HE和SMIRANK的计算效率分别加快了3.9次和13.5次。