Image-based automated haze removal using dark channel prior

Uddin, Mohammad Shorif; Gautam, Bishal; Sarker, Aditi; Akter, Morium; Haque, Mohammad Reduanul

doi:10.1109/r10-htc.2017.8288987

Cited by 1 publication

(3 citation statements)

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“…Haze effect minimization in real scene is very important and it finds wide applications. Previously, we modified a single image haze removal algorithm [11] based on dark channel prior with an automated calculation of patch size and automated handling of sky region's degradation effect, which is known as halo effect.…”

Section: Haze Removal Techniquementioning

confidence: 99%

“…The prior based methods were highly successful in recovering haze-free images. We [11] further improved this method through proposing an adaptive filter-patch to deal with various haze concentrations. However, the effectiveness of these methods is not completely confirmed yet using ground truth (simulated) images.…”

Section: Introductionmentioning

confidence: 99%

“…After generation of haze of different concentrations, we used the dark-channel prior [1] [8] [9] [11] as it is the most prominent method among the dehazing mechanisms.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simulation of Hazy Image and Validation of Haze Removal Technique

Sarker¹,

Akter²,

Uddin³

2019

JCC

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Haze hampers the performance of vision systems. So, removal of haze appearance in a scene should be the first-priority for clear vision. It finds wide spectrum of practical applications. A good number of dehazing techniques have already been developed. However, validation with the help of ground truth i.e. simulated haze on a clear image is an ultimate necessity. To address this issue, in this work synthetic haze images with various haze concentrations are simulated and then used to confirm the validation task of dark-channel dehazing mechanism, as it is a very promising single image dehazing technique. The simulated hazy image is developed using atmospheric model with and without Perlin noise. The effectiveness of dark-channel dehazing method is confirmed using the simulated haze images through average gradient metric, as haze reduces the gradient score.

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Section: Haze Removal Techniquementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%