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The aim is to introduce a dataset for quantitatively evaluating dehazing algorithms and improving image enhancement techniques.

August 17, 2016Open Access

D-HAZY: A dataset to evaluate quantitatively dehazing algorithms

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The aim is to introduce a dataset for quantitatively evaluating dehazing algorithms and improving image enhancement techniques.
Created D-HAZY dataset with over 1400 image pairs of hazy and clear scenes using real images and synthetic haze.
Utilized depth maps from Middelbury and NYU Depth datasets to synthesize high-fidelity hazy images based on physical models.
Conducted quantitative evaluations of multiple single-image dehazing algorithms using the D-HAZY dataset.
D-HAZY dataset contains 1400+ image pairs facilitating improved benchmarking of dehazing techniques.
Comprehensive evaluations reveal significant advancements in dehazing performance metrics compared to previous methods.

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Randomized trial evaluates dehazing techniques using synthetic image dataset, suggesting improved image clarity.

Ancuți et al. (2016) studied this question.

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