Journal ArticleDOI
Fusion-Based Variational Image Dehazing
TLDR
The proposed fusion-based variational image-dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that maximizes contrast and saturation on the hazy input.Abstract:
We propose a novel image-dehazing technique based on the minimization of two energy functionals and a fusion scheme to combine the output of both optimizations. The proposed fusion-based variational image-dehazing (FVID) method is a spatially varying image enhancement process that first minimizes a previously proposed variational formulation that maximizes contrast and saturation on the hazy input. The iterates produced by this minimization are kept, and a second energy that shrinks faster intensity values of well-contrasted regions is minimized, allowing to generate a set of difference-of-saturation (DiffSat) maps by observing the shrinking rate. The iterates produced in the first minimization are then fused with these DiffSat maps to produce a haze-free version of the degraded input. The FVID method does not rely on a physical model from which to estimate a depth map, nor it needs a training stage on a database of human-labeled examples. Experimental results on a wide set of hazy images demonstrate that FVID better preserves the image structure on nearby regions that are less affected by fog, and it is successfully compared with other current methods in the task of removing haze degradation from faraway regions.read more
Citations
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Journal ArticleDOI
Image dehazing by artificial multiple-exposure image fusion.
TL;DR: The obtained results indicate that the fusion of artificially under-exposed images can effectively remove the effect of haze, even in challenging situations where other current image dehazing techniques fail to produce good-quality results.
Proceedings ArticleDOI
On the Duality Between Retinex and Image Dehazing
TL;DR: Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.
Journal ArticleDOI
A Comprehensive Review of Computational Dehazing Techniques
Dilbag Singh,Vijay Kumar +1 more
TL;DR: This paper carries out a comprehensive review of dehazing techniques to show that these could be effectively applied in real-life practice and encourages the researchers to use these techniques for removal of haze from hazy images.
Journal ArticleDOI
Dehazing for Multispectral Remote Sensing Images Based on a Convolutional Neural Network With the Residual Architecture
TL;DR: Experimental results show that the proposed dehazing method can accurately remove the haze in each band of multispectral images under different scenes.
Journal ArticleDOI
Comprehensive survey on haze removal techniques
Dilbag Singh,Vijay Kumar +1 more
TL;DR: The review has revealed that the meta-heuristic techniques can attain the optimistic haze removal parameters and also concurrently develops an optimistic objective function to estimate the depth map efficiently.
References
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Journal ArticleDOI
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