Acceleration of histogram‐based contrast enhancement via selective downsampling
Gang Cao, Huawei Tian, Lifang Yu, Xianglin Huang, Yongbin Wang · IET Image Processing · 2017
The authors propose a general framework to accelerate the universal histogram‐based image contrast enhancement (CE) algorithms. Both spatial and grey‐level selective downsampling 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 proposed to reconstruct the pixel mapping on the grey levels missed by downsampling. As two case studies, the accelerations of histogram equalisation (HE) and the state‐of‐the‐art global CE algorithm, i.e. spatial mutual information and PageRank (SMIRANK), are presented in detail. Both quantitative and qualitative assessment results have verified the effectiveness of their proposed CE acceleration framework. In typical tests, the computational efficiencies of HE and SMIRANK have been increased by about 3.9 and 13.5 times, respectively.