Contrast enhancement technique based on local detection of edges
Computer Vision, Graphics, and Image Processing
Two-dimensional signal and image processing
Two-dimensional signal and image processing
Investigations of image contrast space defined by variations on histogram equalization
CVGIP: Graphical Models and Image Processing
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Contrast enhancement using brightness preserving bi-histogram equalization
IEEE Transactions on Consumer Electronics
Image enhancement based on equal area dualistic sub-image histogram equalization method
IEEE Transactions on Consumer Electronics
Minimum mean brightness error bi-histogram equalization in contrast enhancement
IEEE Transactions on Consumer Electronics
Dnamic contrast enhancement based on histogram specification
IEEE Transactions on Consumer Electronics
Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization
IEEE Transactions on Consumer Electronics
Image enhancement via adaptive unsharp masking
IEEE Transactions on Image Processing
Adaptive image contrast enhancement using generalizations of histogram equalization
IEEE Transactions on Image Processing
Image enhancement by nonlinear extrapolation in frequency space
IEEE Transactions on Image Processing
Transform-based image enhancement algorithms with performance measure
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy
IEEE Transactions on Image Processing
An advanced contrast enhancement using partially overlapped sub-block histogram equalization
IEEE Transactions on Circuits and Systems for Video Technology
A new algorithmic approach for contrast enhancement
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Tree-structured image difference for fast histogram and distance between histograms computation
Pattern Recognition Letters
Enhancing underexposed images preserving the original mood
CCIW'11 Proceedings of the Third international conference on Computational color imaging
Color compensation using nonlinear luminance-RGB component curve of a camera
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Visual impact enhancement via image histogram smoothing and continuous intensity relocation
Computers and Electrical Engineering
High dynamic range optimal fuzzy color image enhancement using Artificial Ant Colony System
Applied Soft Computing
A variational approach for exact histogram specification
SSVM'11 Proceedings of the Third international conference on Scale Space and Variational Methods in Computer Vision
Two-dimensional histogram equalization and contrast enhancement
Pattern Recognition
Image contrast enhancement for preserving mean brightness without losing image features
Engineering Applications of Artificial Intelligence
Exact Histogram Specification for Digital Images Using a Variational Approach
Journal of Mathematical Imaging and Vision
Real-time bio-inspired contrast enhancement on GPU
Neurocomputing
Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients
Digital Signal Processing
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A general framework based on histogram equalization for image contrast enhancement is presented. In this framework, contrast enhancement is posed as an optimization problem that minimizes a cost function. Histogram equalization is an effective technique for contrast enhancement. However, a conventional histogram equalization (HE) usually results in excessive contrast enhancement, which in turn gives the processed image an unnatural look and creates visual artifacts. By introducing specifically designed penalty terms, the level of contrast enhancement can be adjusted; noise robustness, white/black stretching and mean-brightness preservation may easily be incorporated into the optimization. Analytic solutions for some of the important criteria are presented. Finally, a low-complexity algorithm for contrast enhancement is presented, and its performance is demonstrated against a recently proposed method.