Adaptive histogram equalization and its variations

By S. Pizer, E. P. Amburn, John D. Austin, R. Cromartie, A. Geselowitz, Trey Greer, B. T. H. Romeny, John B. Zimmerman. 1987

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

Benchmarking Neural Network Robustness to Common Corruptions and Perturbations (2019, methodology): “For images, we find that preprocessing with Contrast Limited Adaptive Histogram Equalization (Pizer et al., 1987) is quite effective.”
GLADNet: Low-Light Enhancement Network with Global Awareness (2018, methodology): “Histogram equalization (HE) [1] is a widely-used technique.”
Gray-level grouping (GLG): an automatic method for optimized image contrast Enhancement-part I: the basic method (2006, methodology): “…contrast enhancement techniques [7], [22] use multiscale analysis to decompose the image into subbands, and apply corresponding enhancement techniques to the high-frequency subband, and then combine the enhanced high-frequency subband with the low-frequency subband to reconstruct the output image.”
Image Matching Across Wide Baselines: From Paper to Practice (2020, methodology): “Therefore, we experiment with contrast-limited adaptive histogram equalization (CLAHE) [71], as implemented in OpenCV.”
Benchmarking Neural Network Robustness to Common Corruptions and Surface Variations (2018, methodology): “For images, we find that preprocessing with Contrast Limited Adaptive Histogram Equalization [48] is quite effective.”
A Multi-Organ Nucleus Segmentation Challenge (2020, methodology): “Six teams also used either contrast enhancement (or histogram equalization), among which CLAHE [38] was the most commonly used technique.”
A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs (2007, methodology): “The compared algorithms were: ANCE [29], adaptive unsharp masking [40], contrast-limited adaptive histogram equalization [41], and wavelet-based enhancement [42].”
FSDR: Frequency Space Domain Randomization for Domain Generalization (2021, methodology): “Different from existing domain randomization [77] that employs GANs for image stylization, we adopt histogram matching [48] for online image translation.”
EnlightenGAN: Deep Light Enhancement Without Paired Supervision (2019, methodology): “Low-light image image enhancement has been actively studied as an image processing problem for long, with a few classical methods such as the adaptive histogram equalization (AHE) [3], Retinex [2] and multi-scale Retinex model [18].”
Learning to Restore Low-Light Images via Decomposition-and-Enhancement (2020, background): “Represented by histogram equalization [36] and gamma correction, global and local contrast enhancement operators are proposed based on detecting semantic regions (e.”
Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement (2004, methodology): “Combination of this contrast limiting approach with the aforementioned adaptive histogram equalization re-sults in what is referred to as Contrast Limited Adaptive Histogram Equalization (CLAHE) discussed in [1] and summarized in [2].”
Breast cancer detection using deep convolutional neural networks and support vector machines (2019, methodology): “It is an excellent contrast enhancement method for both natural and medical images ( Pizer et al., 1987 ) and ( Pisano et al., 1998 ).”
Image processing of multiphase images obtained via X‐ray microtomography: A review (2014, methodology): “Second, the impact of imbalanced class probabilities is reduced by histogram clipping [Pizer et al., 1987].”