Referenceless Prediction of Perceptual Fog Density and Perceptual Image Defogging

By Lark Kwon Choi, Jaehee You, A. Bovik. IEEE Transactions on Image Processing, 2015

759 citations recorded by the source corpus.

Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/c299f73ff1e50f91f64700e07dea1371d4afcafa

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In Computer Graphics and Computer-Aided Design

In Computer Vision and Pattern Recognition

Citations in the corpus

NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images (2020, methodology): “[17] introduced Fog Aware Density Evaluator (FADE), a blind measure, which aims to predict the visibility of a hazy scene from a single image without using a haze-free (reference) image.”
Overview of Environment Perception for Intelligent Vehicles (2017, methodology): “In [13], a perceptual fog density prediction model was proposed by using natural scene statistics and”
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images (2018, background): “Fusionbased single image dehazing approaches [1, 12] have also achieved visually pleasant results without explicit transmission estimation and, more recently, several machine learning based methods have been introduced [40, 8, 34].”
I-HAZE: a dehazing benchmark with real hazy and haze-free indoor images (2018, background): “Fusion-based single image dehazing approaches [13], [23] have also achieved visually pleasant results without explicit transmission estimation and, more recently, several machine learning based methods have been introduced [17], [24], [25].”
Learning a No-Reference Quality Assessment Model of Enhanced Images With Big Data (2018, neutral): “Similar to [47], we compute contrast energy on three channels C E f = α · Y (s f ) Y (s f ) + α · θ − φ f (3)”
D-HAZY: A dataset to evaluate quantitatively dehazing algorithms (2016, methodology): “The method has been extended recently by Choi et al. [20].”
Model Adaptation with Synthetic and Real Data for Semantic Dense Foggy Scene Understanding (2018, methodology): “Since annotating images in a ﬁne-grained manner regarding fog density is very challenging, previous methods are trained on a few hundreds of images divided into only two classes: foggy and fog-free [10].”
Gated Fusion Network for Single Image Dehazing (2018, methodology): “Another line of research tries to make use of a fusion principle to restore hazy images in [1, 5].”
Dense-Haze: A Benchmark for Image Dehazing with Dense-Haze and Haze-Free Images (2019, background): “Another category is represented by multi-scale fusion approaches [17], [18] that enhance the hazy scenes without explicitly estimating the transmission map.”