Minimum mean brightness error bi-histogram equalization in contrast enhancement

By Soong-Der Chen, A. Ramli. IEEE Trans. Consumer Electron., 2003

1,083 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/370960e958371649bc8a0afb3176f4e80ac36348

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In Computer Vision and Pattern Recognition

Citations in the corpus

A Dynamic Histogram Equalization for Image Contrast Enhancement (2007, background): “Some researches have also focused on improvement of histogram equalization based contrast enhancement such as mean preserving bi-histogram equalization (BBHE) [9], equal area dualistic sub-image histogram equalization (DSIHE) [15] and minimum mean brightness error bi-histogram equalization (MMBEBHE)”
Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization (2007, methodology): “2, we show results from BBHE [6], MMBEBHE [8] and the proposed WTHE with r=0.”
An Underwater Color Image Quality Evaluation Metric (2015, background): “image quality such as entropy [9], [10], brightness [11]–[13], sharpness [14], [15], contrast [14] or colourfulness [16].”
Contrast Enhancement Based on Layered Difference Representation of 2D Histograms (2013, methodology): “5 in all experiments to provide the best overall TABLE I OBJECTIVE QUALITY ASSESSMENT OF CE ALGORITHMS USING FOUR METRICS: DISCRETE ENTROPY (DE) [29], MEASURE OF ENHANCEMENT (EME) [30], ABSOLUTE MEAN BRIGHTNESS ERROR (AMBE) [10], AND PIXDIST [31].”
Brightness preserving histogram equalization with maximum entropy: a variational perspective (2005, methodology): “MMBEBHE [ 5 ] is another modification to two-pieceseparating histogram equalization algorithm.”
Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement (2007, background): “Next, in 1999, dualistic sub-image histogram equalization (DSIHE) has been proposed by Wan et al [4].”
Bi-histogram equalization with a plateau limit for digital image enhancement (2009, background): “BBHE, DSIHE and MMBEBHE present the bisections MBPHE.”
An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, background): “This metric represents the direct deviation between the enhanced image and the original image; it has the same meaning as the absolute mean brightness error (AMBE) [75]. where M and N are the width and height, respectively, of the image; f ( i , j ) represents the input image; and f e ( i , j ) repr”
Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving (2007, methodology): “Images were extracted from the CVG-UGR database [11] and provided by the authors of [3], [7].”
Brightness preserving dynamic fuzzy histogram equalization (2010, methodology): “To overcome this limitation, several brightness preserving histogram modification approaches, such as bi-histogram equalization (BBHE [2], MMBEBHE [3]), multi-histogram equalization (DHE [4], BPDHE [5]) and histogram specification (BPHEME [6]) have been proposed in literature.”
Recursively separated and weighted histogram equalization for brightness preservation and contrast enhancement (2008, methodology): “As test images, we selected 10 images: couple, F16, jet, bottle, house, woman, U2, girl, Einstein, and copter, which have been previously used in many other methods to show their performances [1], [3], [4], [7].”