Exact histogram specification

By Dinu Coltuc, P. Bolon, Jean-Marc Chassery. IEEE Transactions on Image Processing, 2006

390 citations recorded by the source corpus.

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The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation (2021, background): “Histogram distribution of images is adjusted at both global [12], [13] and local levels [14], [15].”
Low-Light Image Enhancement via a Deep Hybrid Network (2019, methodology): “Global based HE methods [27]–[30] use the histogram information of the entire input image for the transformation function estimation.”
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement (2020, background): “Histogram distribution of images is adjusted at both global [6, 9] and local levels [23, 13].”
Controlling low-level image properties: The SHINE toolbox (2010, background): “, 2009), but different approaches have been suggested (Coltuc et al., 2006; Wan & Shi, 2007).”
The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis (2020, methodology): “Another global color normalization technique is known as histogram specification [29,48].”
A retinex-based enhancing approach for single underwater image (2014, methodology): “According to the definition of the Cumulative Density Functions (CDF) [17][18], the CDF of I ′ is: C(z) = z∑ i=0 I ′(i) · n(i) max(z)∑ i=0 I ′(i) · n(i) (8) where z is the zth gray level of I , max(z) is the maximum gray level of I , n is the number of the zth gray level.”