A simple and effective histogram equalization approach to image enhancement

By Heng-Da Cheng, Xiangjun Shi. Digit. Signal Process., 2004

477 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1c5749e68ff8f9e75bb5e655a1b49287ce076ce0

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In Computer Vision and Pattern Recognition

Citations in the corpus

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.”
Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement (2020, methodology): “Here a log scale is used on the x-axis for illustration. togram equalization [25, 4, 40] or gamma correction [11, 26, 32] to enhance low-light images.”
Approaches for automated detection and classification of masses in mammograms (2006, unknown): “To improve HE method, Multi-peak HE method [85,179] has been developed.”
Low-Light Image Enhancement via a Deep Hybrid Network (2019, background): “Early approaches [9]–[13] focus on contrast enhancement to recover the visibility of dark regions.”
LIME: Low-Light Image Enhancement via Illumination Map Estimation (2017, background): “[3], [4], [5] can avoid the above problem by somehow forcing the output image to fall in the range [0] [1].”
Automated breast cancer detection and classification using ultrasound images: A survey (2015, background): “The multi-peak generalized histogram equalization was proposed in [85].”
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement (2023, methodology): “Plain methods like histogram equalization [1, 7, 11, 40, 41] and Gama Correction (GC) [18, 42, 52] directly amplify the low visibility and contrast of underexposed images.”
STAR: A Structure and Texture Aware Retinex Model (2019, methodology): “We compare the proposed STAR model with previous competing low-light image enhancement methods, including HE [47], MSRCR [19], Contextual and Variational Contrast (CVC) [48], Naturalness Preserved Enhancement (NPE) [5], LDR [49], SIRE [32], MF [50], WVM [7], LIME [6], and JieP [8].”