Adaptive image contrast enhancement using generalizations of histogram equalization

By J. Alex Stark. IEEE Transactions on Image Processing, 2000

1,272 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/2865ded514bceaf56bfcac95de8a1fa354e04aa9

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In Computer Vision and Pattern Recognition

Citations in the corpus

Reversible data hiding: Advances in the past two decades (2016, background): “In principle, image contrast enhancement can be achieved by histogram equalization [140].”
Detection of Red lesions in diabetic retinopathy affected fundus images (2015, methodology): “To enhance contrast adaptive histogram equalization (AHE) technique is used [9] [10].”
Color and Contrast Enhancement by Controlled Piecewise Affine Histogram Equalization (2012, methodology): “The local method proposed by Stark [2] modifies the cumulative distribution function of the histogram to adjust the level of enhancement.”
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation (2021, background): “Histogram distribution of images is adjusted at both global [12], [13] and local levels [14], [15].”
A Generalized Unsharp Masking Algorithm (2011, background): “It is difficult to see the details in a low contrast image.”
Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method (2022, methodology): “Traditional LLIE methods are mainly based on image priors or physical models from other tasks, such as histogram equalization-based meth-ods (Kim 1997; Stark 2000), retinex-based methods (Kimmel et al. 2003; Wang et al. 2014) and dehazing-based meth-ods (Dong et al. 2011; Zhang et al. 2012).”
A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement (2017, methodology): “Local histogram equalization [36], [37] adopts the sliding window strategy to perform HE locally.”
Fast Image/Video Contrast Enhancement Based on Weighted Thresholded Histogram Equalization (2007, methodology): “The AHE methods usually have high computational complexity and some of them suffer from block-related artifacts.”
A simple and effective histogram equalization approach to image enhancement (2004, methodology): “Another method uses local histogram modification to enhance image contrast in a local area [1,2,6,11,16,19,21,26], such as: (1) local”
Low-Light Image Enhancement via a Deep Hybrid Network (2019, methodology): “To overcome this problem, local based HE approaches [10], [31], [32] use a sliding window over the image and only take pixels inside this window into account.”
Image enhancement by unsharp masking the depth buffer (2006, methodology): “Global methods mainly represent histogram modifications, see e.g. [Hummel 1975; Stark 2000].”
Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, methodology): “The method can be generalized such that any transition between global and local contrast enhancement is achieved [Stark, 2000].”
Multipurpose watermarking for image authentication and protection (2001, background): “as the one proposed in [31] should be considered as really inci-”