Vision Enhancement in Homogeneous and Heterogeneous Fog

By Jean-Philippe Tarel, Nicolas Hautière, Laurent Caraffa, Aurélien Cord, Houssam Halmaoui, Dominique Gruyer. IEEE Intelligent Transportation Systems Magazine, 2012

414 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/9a5636eb2cf8d4f8fd281dbf4ddf23c0651994d0

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

Color Balance and Fusion for Underwater Image Enhancement (2018, background): “coefficient as long as the model parameters are estimated locally [20], [21], [25].”
D-HAZY: A dataset to evaluate quantitatively dehazing algorithms (2016, background): “The FRIDA dataset [27] designed for Advanced Driver Assistance Systems (ADAS) is a synthetic image database (computer graphics generated scenes).”
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images (2018, methodology): “The work [31] presents the FRIDA dataset designed for Advanced Driver Assistance Systems (ADAS) that is a synthetic image database (computer graphics generated scenes) with 66 roads synthesized scenes.”
Single Image Dehazing via Multi-scale Convolutional Neural Networks with Holistic Edges (2019, methodology): “Nevertheless, the proposedmethod is still faster than most other methods (Fattal 2008; He et al. 2011; Tarel et al. 2012; Meng et al. 2013; Berman et al. 2016).”
Benchmarking Single-Image Dehazing and Beyond (2017, background): “Synthetic Real indoor outdoor outdoor annotated Fattal [37] 4 8 31 FIRDA [39] 480 Ma [33] 3 22 HazeRD [46] 14 Sakaridis [34] - 25,000 101 101 RESIDE 14,490 72,135 9,129 4,322”
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding (2018, background): “Certain works focus particularly on enhancing foggy road scenes [38, 54].”
Dense-Haze: A Benchmark for Image Dehazing with Dense-Haze and Haze-Free Images (2019, background): “Tarel et al. [26] introduced the ﬁrst synthetic dehazing dataset (FRIDA) which contains a r X i v : 1904 .”
Cycle-Dehaze: Enhanced CycleGAN for Single Image Dehazing (2018, background): “The proliferation of deep neural networks increases the use of large-scale datasets, therefore, researchers tend to create synthetic dehazing datasets like FRIDA [33] and D-HAZY [2] , which have a more practical creation process than real dehazing datasets.”
Investigating Haze-Relevant Features in a Learning Framework for Image Dehazing (2014, background): “[20] adds planar constraint for road images; Caraffa et al.”
Single Image Dehazing via Multi-scale Convolutional Neural Networks (2016, methodology): “The proposed algorithm is more efficient than the state-of-the-art image dehazing methods [1,11,23,25,27] in terms of run time.”
Semantic Foggy Scene Understanding with Synthetic Data (2017, neutral): “Certain works focus particularly on enhancing foggy road scenes [49,65].”
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images (2018, background): “Today, all assessment datasets[42, 4, 46] rely on synthesized hazy images using a simplified optical model, and known depth.”
NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images (2020, background): “[41] introduced FRIDA, one of the first synthetic image dehazing datasets.”