Dehazing image using analytical model and color attenuation prior

Kaur, Satinderpal; Bansal, Meenakshi; Bathla, Ashok Kumar

doi:10.1109/ngct.2016.7877508

Cited by 2 publications

(1 citation statement)

References 15 publications

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“…The goal of image dehazing is therefore formulated as the successful recovery of the J, t, and A parameters from the acquired image, I. Numerous schemes have been previously proposed towards image enhancement via dehazing including but not limited to Retinex [9], Guided Filters [10], Wavelet based fusion [11] and analytical color modeling [12]. Most of such early schemes have relied extensively on multi-image prior schemes and fail when such requirements are unsatisfied.…”

Section: Introductionmentioning

confidence: 99%

Feature-Centric Image Enhancement via Dehazing

Owusu-Agyeman¹,

Xie²,

Yeboah³

2018

IJCEE

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In this paper, we address the image dehazing problem through a global feature-restoration pipeline. We propose a dark channel prior-based global image dehazing algorithm which captures and restores the true features of pixels within haze-degraded regions by applying scene depth selection and adaptive filtering. Our scheme harnesses haze and depth features intuitively across a given image without the prior scene depth information. This allows our scheme to sustain a high dehazing efficiency across all image regions irrespective of the local depth variations. We prove that haze degradation is linearly correlated with scene depth and based on this nuance, propose a depth selection and cropping scheme, which guides the adaptive filter iteratively across the image. Secondly, we put forward haze relevant image features and highlight the dark-channel prior for image dehazing. We merge the dark channel prior and scene depth-cropping schemes into a unified dehazing pipeline which is capable of sustaining uniform and robust results across all image regions, in real-time. We verify the superiority of the proposed scheme in terms of speed and robustness through computer-based experiments. Finally, we present comparison results with state-of-the-art and further highlight the comparative superiority of our scheme.

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