Contrast enhancement technique based on local detection of edges
Computer Vision, Graphics, and Image Processing
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Edge-avoiding wavelets and their applications
ACM SIGGRAPH 2009 papers
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
Contrast enhancement using brightness preserving bi-histogram equalization
IEEE Transactions on Consumer Electronics
Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering
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
Multi-Histogram Equalization Methods for Contrast Enhancement and Brightness Preserving
IEEE Transactions on Consumer Electronics
A multiscale retinex for bridging the gap between color images and the human observation of scenes
IEEE Transactions on Image Processing
Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy
IEEE Transactions on Image Processing
Enhancement of Color Images by Scaling the DCT Coefficients
IEEE Transactions on Image Processing
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In this paper, we propose a two-dimensional histogram equalization (2DHE) algorithm which utilizes contextual information around each pixel to enhance the contrast of an input image. The algorithm is based on the observation that the contrast in an image can be improved by increasing the grey-level differences between each pixel and its neighbouring pixels. The image equalization is achieved by assuming that for a given image, the modulus of the grey-level differences between pixels and their neighbouring pixels are equally distributed. The well-known global histogram equalization algorithm is a special case of 2DHE when contextual information is not utilized. 2DHE is easy to implement requiring only a small number of simple arithmetic operations and is thus suitable for real-time contrast enhancement applications. Experimental results show that 2DHE produces better or comparable enhanced images than several state-of-the-art algorithms. The only parameter in 2DHE which requires tuning is the size of the spatial neighbourhood support which provides the contextual information for a given dynamic range of the enhanced image. An automated parameter selection algorithm is also presented. The algorithm can be applied to a wide range of image types.