A fusion-based enhancing method for weakly illuminated images

By Xueyang Fu, Delu Zeng, Yue Huang, Yinghao Liao, Xinghao Ding, John William Paisley. Signal Processing, 2016

681 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/741333428f79185d9aa33701cebaf1b0bc33127d

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In Atomic and Molecular Physics, and Optics

In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Learning a Simple Low-Light Image Enhancer from Paired Low-Light Instances (2023, background): “To correct contrast, uncover textures, and remove sensor noise, great efforts have been made in developing Low-light Image Enhancement (LIE) algorithms in the past decades [1,5,6,8,28,35].”
Low-Light Image Enhancement With Semi-Decoupled Decomposition (2020, methodology): “Third, among all the eight methods for comparison, the results based on JieP [4], HQEC [5], MF [10] and OurI all achieve acceptable visual quality.”
Attention Guided Low-Light Image Enhancement with a Large Scale Low-Light Simulation Dataset (2019, background): “MF [22] processes the illumination map in a multi-scale fashion to improve the local contrast and maintain naturalness.”
SNR-Aware Low-light Image Enhancement (2022, methodology): “We compare our method with a rich collection of SOTA methods for low-light image enhancement, including Dong [9], LIME [15], MF [11], SRIE [12], BIMEF [55], DRD [45], RRM [28], SID [5], DeepUPE [40], KIND [61], DeepLPF [33], FIDE [48], LPNet [26], MIR-Net [59], RF [24], 3DLUT [60], A3DLUT [42],…”
LIME: Low-Light Image Enhancement via Illumination Map Estimation (2017, methodology): “Fu et al. proposed a method to adjust the illumination by fusing multiple derivations of the initially estimated illumination map (MF) [12].”
Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Method (2022, methodology): “…including seven traditional non-learning methods (BIMEF (Ying, Li, and Gao 2017), FEA (Dong et al. 2011), LIME (Guo, Li, and Ling 2016), MF (Fu et al. 2016a), NPE (Wang et al. 2013), SRIE (Fu et al. 2016b), MSRCR (Jobson, Rahman, and Woodell 1997)), three supervised CNN-based methods…”
STAR: A Structure and Texture Aware Retinex Model (2019, methodology): “TABLE I: Average NIQE [51] and VIF [52] results of different methods on 35 low-light images collected from [6]–[8], [32], [50] and 200 low-light images provided in [5].”
Deep Retinex Decomposition for Low-Light Enhancement (2018, methodology): “SRIE [7] estimates reﬂectance and illumination simultaneously using a weighted variational model.”
Sparse Gradient Regularized Deep Retinex Network for Robust Low-Light Image Enhancement (2021, background): “Fu et al. [7] proposed an improved version by fusing different merits into a single one based on multiple derivatives of the estimated illumination.”
An Experiment-Based Review of Low-Light Image Enhancement Methods (2020, methodology): “[219] proposed an image enhancement algorithm based on the fusion of the results of multiple enhancement techniques.”
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (2020, methodology): “…Limited Adaptive Histogram Equalization (CLAHE) [36], Low-Light Image Enhancement via Illumination Map Estimation (LIME) [9], Multiple Fusion (MF) [7], Multiscale Retinex (MR) [13] Joint Enhancement and Denoising Method (JED) [25], Reﬁned Retinex Model (RRM) [18], Simultaneous Reﬂectance and…”
Low-Light Image Enhancement via a Deep Hybrid Network (2019, methodology): “Since HE based methods lack physical explanation, a physically meaningful model [2], [33] is proposed for image enhancement.”
R2RNet: Low-light Image Enhancement via Real-low to Real-normal Network (2021, background): “MF [7] tried to improve the local contrast of the illumination map and maintain naturalness.”
Getting to Know Low-light Images with The Exclusively Dark Dataset (2018, methodology): “Nevertheless, it was noted that in the effort to enhance low-light images, noise is often ampliﬁed and negatively impacts the result quality ([17], [13], [11], [18]).”
Advancing Image Understanding in Poor Visibility Environments: A Collective Benchmark Study (2019, methodology): “13 We also include seven state-of-the-art algorithms for light/contrast enhancement: (a) Bio-Inspired Multi-Exposure Fusion (BIMEF) [151]14; (b) Dehazing [152]14; (c) Lowlight IMage Enhancement (LIME) [107]15; (d) MF [106]14; (e) Multi-Scale Retinex (MSR) [104]14; (f) Joint Enhancement and Denoising”
MBLLEN: Low-Light Image/Video Enhancement Using CNNs (2018, methodology): “Input MF [11] LIME [14] Ying [43] BIMEF [41] Ours In Table 3, our method almost achieves all the best results under all quality metrics.”