Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy

By Sos S. Agaian, Blair Silver, Karen A. Panetta. IEEE Transactions on Image Processing, 2007

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An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, background): “…the no-reference image quality metric for contrast distortion (NIQMC) [294], the Global Contrast Factor (GCF) [295], the average information content (AIC) [296], the effective measure of enhancement (EME) [297], PixDist [298], and the no-reference free-energy-based robust metric (NFERM) [299].”
Toward Fast, Flexible, and Robust Low-Light Image Enhancement (2022, neutral): “We used two full-reference metrics including PSNR and SSIM, ﬁve no-reference metrics including DE [20], EME [1], LOE [23] and NIQE [23].”
Video Denoising, Deblocking, and Enhancement Through Separable 4-D Nonlocal Spatiotemporal Transforms (2012, methodology): “Among the existing enhancement techniques we mention methods based on histogram manipulation [35], linear and non-linear unsharp masking [36], [37], [38], fuzzy logic [39], and weighted median filter [40], [41].”
Contextual and Variational Contrast Enhancement (2011, methodology): “For color images, the contrast enhancement is quantified by computing these measures on their luminance channel only.”
An Underwater Color Image Quality Evaluation Metric (2015, background): “The widely used quality metrics for grayscale images are contrast [19]–[21] or edge sharpness [22]–[24].”