A Histogram Modification Framework and Its Application for Image Contrast Enhancement

By Tarik Arici, Salih Dikbas, Yucel Altunbasak. IEEE Transactions on Image Processing, 2009

951 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/b0f6c33a4f7ee4e5e15aa2c6ce8b720850bbc09b

How researchers see this paper

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

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement (2016, methodology): “[39] designed the histogram modification framework (HMF) by first seeking an intermediate histogram h between the input histogram hi and the uniformly distributed histogram u by minimizing a weighted distance ||h − hi|| + λ||h − u|| before performing HE of h.”
Color and Contrast Enhancement by Controlled Piecewise Affine Histogram Equalization (2012, methodology): “[3] propose a global method to modify the histogram by solving an optimization problem.”
Contrast Enhancement Based on Layered Difference Representation of 2D Histograms (2013, methodology): “The layered difference representation (LDR): the transformation function x = [x0, x1, · · · , x255]T and the difference variables dlk’s are shown in a tree-like pyramidal structure.”
No-Reference Quality Assessment of Contrast-Distorted Images Based on Natural Scene Statistics (2015, background): “The limitation of the acquisition device or poor lighting condition would cause the loss of contrast and visible details in images [9].”
Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution (2013, methodology): “Quantitative evaluation of contrast enhancement is not an easy task, because an acceptable criterion by which to quantify the improved perception has yet to be proposed [1], [17], [18].”
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, methodology): “[4] propose to analyze and penalize the unnatural visual effects for better visual quality; DHECI [54] introduces and uses the differential gray-level histogram; CVC [9] uses the interpixel contextual information; LDR [46] focuses on the layered difference representation of 2D histogram to try to en”
MBLLEN: Low-Light Image/Video Enhancement Using CNNs (2018, methodology): “PSNR SSIM [39] VIF [32] LOE TMQI [40] AB [6] WAHE [2] 17.”
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (2018, background): “Moreover, the BIQME works without original references and this makes it apply to many kinds of images, as opposed to some recent works that are only available for enhancing natural images [63], [34].”
Learning a Deep Single Image Contrast Enhancer from Multi-Exposure Images (2018, methodology): “Traditional single image contrast enhancement (SICE) techniques include those histogram-based algorithms [4]–[6], which increase the contrast of an image by redistributing the luminous intensity on histogram, and Retinex based algorithms [7]–[9], which enhance the reflectance and illumination compon”
Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances (2023, background): “[1] introduced penalty terms to avoid the unnatural look and visual artifacts of the enhanced image.”
A fusion-based enhancing method for weakly illuminated images (2016, methodology): “Variational methods [9,10] use different regularization terms on the histogram.”
Low-Light Image Enhancement via a Deep Hybrid Network (2019, background): “Early approaches [9]–[13] focus on contrast enhancement to recover the visibility of dark regions.”