Content-based image enhancement in the compressed domain based on multi-scale α-rooting algorithm
Pattern Recognition Letters
Integrated Computer-Aided Engineering
A solution to the deficiencies of image enhancement
Signal Processing
A histogram modification framework and its application for image contrast enhancement
IEEE Transactions on Image Processing
An image contrast enhancement method based on genetic algorithm
Pattern Recognition Letters
Global and local contrast enhancement for image by genetic algorithm and wavelet neural network
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
Short Communication: Histogram Modified Local Contrast Enhancement for mammogram images
Applied Soft Computing
Visual impact enhancement via image histogram smoothing and continuous intensity relocation
Computers and Electrical Engineering
ACII'05 Proceedings of the First international conference on Affective Computing and Intelligent Interaction
Gaussian mixture modeling of histograms for contrast enhancement
Expert Systems with Applications: An International Journal
Contrast enhancement using adaptively modified histogram equalization
PSIVT'06 Proceedings of the First Pacific Rim conference on Advances in Image and Video Technology
Two-dimensional histogram equalization and contrast enhancement
Pattern Recognition
International Journal of Imaging Systems and Technology
Contrast brushes: interactive image enhancement by direct manipulation
Computational Aesthetics'09 Proceedings of the Fifth Eurographics conference on Computational Aesthetics in Graphics, Visualization and Imaging
Image enhancement using Exposure based Sub Image Histogram Equalization
Pattern Recognition Letters
Fast image enhancement in compressed wavelet domain
Signal Processing
Journal of Intelligent Manufacturing
The Visual Computer: International Journal of Computer Graphics
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Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extends. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE) to provide maximum brightness preservation. BBHE separates the input image's histogram into two based on input mean before equalizing them independently. This paper proposes to perform the separation based on the threshold level, which would yield minimum absolute mean brightness error (AMBE - the absolute difference between input and output mean). An efficient recursive integer-based computation for AMBE has been formulated to facilitate real time implementation. Simulation results using sample image which represent images with very low, very high and medium mean brightness show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), can be properly enhanced by MMBEBHE. Besides, MMBEBHE also demonstrate comparable performance with BBHE and DSIHE when come to use the sample images show in [Yeong-Taeg Kim, February 1997] and [Yu Wan et al., October 5 1999].