Contrast Enhancement Based on Layered Difference Representation of 2D Histograms

By Chulwoo Lee, Chang-Su Kim. IEEE Transactions on Image Processing, 2013

1,000 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/f048dce88f1de442a4f7aede4f5724dcf75965e2

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Citations in the corpus

LIME: Low-Light Image Enhancement via Illumination Map Estimation (2017, methodology): “For instance, contextual and variational contrast enhancement (CVC) [6] tries to find a histogram mapping that pays attention on large gray-level differences, while the work [7] achieves improvement by seeking a layered difference representation of 2D histograms (LDR).”
Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances (2023, methodology): “[14] applied the layered difference representation of 2D histograms to amplify the graylevel differences between adjacent pixels.”
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.”
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.”
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement (2023, methodology): “In addition to the above eight benchmarks, we test our method on five datasets: LIME [18], NPE [50], MEF [36], DICM [28], and VV [47] that have no ground truth.”
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, methodology): “[4] propose to analyze and penalize the unnatural visual effects for better visual quality; DHECI [54] introduces and uses the differential gray-level histogram; CVC [9] uses the interpixel contextual information; LDR [46] focuses on the layered difference representation of 2D histogram to try to en”
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (2020, background): “Beside, the results of NPE [28] and DICM [16] are also provided in the supplementary material.”
Low-Light Image Enhancement With Semi-Decoupled Decomposition (2020, methodology): “Histogram-based methods [9] aim at optimizing the shape of an image histogram, which only counts pixel intensities but ignores their spatial information.”
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].”
Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement (2021, methodology): “The noise, including compression artifacts, are successfully [43].”
A fusion-based enhancing method for weakly illuminated images (2016, methodology): “In this experiment, we use 40 different kinds of weakly illumination images and enhance them using [1,10,16,20,22,23] and our proposed method.”
MBLLEN: Low-Light Image/Video Enhancement Using CNNs (2018, methodology): “…for better visual quality; DHECI [29] introduces and uses the differential gray-level histogram; CVC [5] uses the interpixel contextual information; LDR [26] focuses on the layered difference representation of 2D histogram to try to enlarge the gray-level differences between adjacent pixels.”
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].”
Beyond Brightening Low-light Images (2021, methodology): “Speciﬁcally, KinD++ outperforms all the competitors on the LIME (with # 6 image excluded), NPE and DICM datasets.”
Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement (2020, background): “Histogram distribution of images is adjusted at both global [6, 9] and local levels [23, 13].”