A Dynamic Histogram Equalization for Image Contrast Enhancement

By Mohammad Abdullah-Al-Wadud, Hasanul Kabir, M. Ali Akber Dewan, Oksam Chae. IEEE transactions on consumer electronics, 2007

1,224 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/39ea5cdda15e571acb86b9e7144bd8dbabb3f1db

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In Computer Vision and Pattern Recognition

Citations in the corpus

Brightness preserving dynamic fuzzy histogram equalization (2010, methodology): “To overcome this limitation, several brightness preserving histogram modification approaches, such as bi-histogram equalization (BBHE [2], MMBEBHE [3]), multi-histogram equalization (DHE [4], BPDHE [5]) and histogram specification (BPHEME [6]) have been proposed in literature.”
Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement (2021, background): “Later on, histogram equalization (HE) methods stretch the dynamic range of an image [1], [2].”
Kindling the Darkness: A Practical Low-light Image Enhancer (2019, background): “One technical line, with histogram equalization (HE) [1], [2], [3] and its follow-ups [4], [5] as representatives, tries to map the value range into [0, 1] and balance the histogram of outputs for avoiding the truncation problem.”
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019, background): “The resolution of most faces in our dataset is below 300 pixel(2) and the the face number mostly falls into the range [1, 20].”
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.”
Beyond Brightening Low-light Images (2021, background): “One technical line, with histogram equalization (HE) (Pisano et al. 1998; Cheng and Shi 2004; Abdullah-Al-Wadud et al. 2007) and its follow-ups (Turgay and Tardi 2011; Lee et al. 2013) as representatives, tries to map the value range into [0, 1] and balance the histogram of outputs for avoiding the…”
Brightness Preserving Dynamic Histogram Equalization for Image Contrast Enhancement (2007, methodology): “RMSHE, RSIHE, and MPHEBP) tends to not allow some sections of the histogram to expand, as explained by Fig. 2. Thus, Wadud et al [ 10 ] has proposed dynamic histogram equalization (DHE) for used in image contrast enhancement.”
LR3M: Robust Low-Light Enhancement via Low-Rank Regularized Retinex Model (2020, methodology): “A dynamic HE method [2] is developed to perform contrast enhancement on low-light images.”
LIME: Low-Light Image Enhancement via Illumination Map Estimation (2017, background): “Histogram equalization (HE) strategies [3]–[5] can avoid the above problem by somehow forcing the output image to fall in the range [0, 1].”
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.”
An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, background): “…subimage histogram equalization (RSIHE) [92], entropy-based dynamic subhistogram equalization (EDSHE) [93], dynamic histogram equalization (DHE) [94], brightness-preserving dynamic histogram equalization (BPDHE) [95], bi-histogram equalization with a plateau limit (BHEPL) [96],…”
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (2020, methodology): “His-togram equalization (HE) methods [23, 1] enhance the low-light images by stretching the dynamic range, providing undesirable illumination with unintentionally ampliﬁed intensive noise.”