Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling
This work addresses computational efficiency for image processing applications, but it is incremental as it builds on existing contrast enhancement methods.
The paper tackles the problem of accelerating histogram-based contrast enhancement algorithms by proposing a framework using selective downsampling and mapping function calibration, resulting in speedups of about 3.9 times for histogram equalization and 13.5 times for SMIRANK.
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.