Single Image Haze Removal Using Dark Channel Prior

Underwater images typically exhibit color distortion and low contrast as a result of the exponential decay that light suffers as it travels. Moreover, colors associated to different wavelengths have different attenuation rates, being the red wavelength the one that attenuates the fastest. To restore underwater images, we propose a Red Channel method, where colors associated to short wavelengths are recovered, as expected for underwater images, leading to a recovery of the lost contrast. The Red Channel method can be interpreted as a variant of the Dark Channel method used for images degraded by the atmosphere when exposed to haze. Experimental results show that our technique handles gracefully artificially illuminated areas, and achieves a natural color correction and superior or equivalent visibility improvement when compared to other state-of-the-art methods.

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“…Proof: It is a simple adaptation of the steps of the derivation in (17). For details, see the Appendix.…”

Section: Transmission Estimate From Red Channelmentioning

confidence: 99%

Automatic Red-Channel underwater image restoration

Galdrán

Pardo

Picón³

et al. 2015

Journal of Visual Communication and Image Representation

767

420

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“…Li et al (2015) perform single image dehazing using the change of detail prior. He et al (2011) proposed a dark channel prior (DCP) by noticing that in haze-free images, in every pixel's regional neighborhood at least one color channel contains pixels with very low intensity. The low intensity is caused either by shadows cast by objects or by very bright colors that have a high intensity in only one or two color channels.…”

Section: Dehazingmentioning

confidence: 99%

“…The BCP underwater dehazing method was presented by Gao et al (2016) and originates from a dehazing technique for atmospheric images developed in He et al (2011). He et al observe that one of the color channels of a local area in hazefree images has low intensity and call this assumption the Dark Channel Prior.…”

Section: Bright Channel Prior (Bcp)mentioning

confidence: 99%

Impact of Dehazing on Underwater Marker Detection for Augmented Reality

et al. 2018

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Underwater augmented reality is a very challenging task and amongst several issues, one of the most crucial aspects involves real-time tracking. Particles present in water combined with the uneven absorption of light decrease the visibility in the underwater environment. Dehazing methods are used in many areas to improve the quality of digital image data that is degraded by the influence of the environment. This paper describes the visibility conditions affecting underwater scenes and shows existing dehazing techniques that successfully improve the quality of underwater images. Four underwater dehazing methods are selected for evaluation of their capability of improving the success of square marker detection in underwater videos. Two reviewed methods represent approaches of image restoration: Multi-Scale Fusion, and Bright Channel Prior. Another two methods evaluated, the Automatic Color Enhancement and the Screened Poisson Equation, are methods of image enhancement. The evaluation uses diverse test data set to evaluate different environmental conditions. Results of the evaluation show an increased number of successful marker detections in videos pre-processed by dehazing algorithms and evaluate the performance of each compared method. The Screened Poisson method performs slightly better to other methods across various tested environments, while Bright Channel Prior and Automatic Color Enhancement shows similarly positive results.

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“…In this second phase, the dark channel priori approach originally proposed in [14], is applied in extracting the dark channel components of the input image (the output from the first stage of the algorithm). The dark channel is computed as follows:…”

Section: Estimation Of Dark Channel Priorimentioning

confidence: 99%

A Novel Dark-Channel Dehazing Algorithm Based on Adaptive-Filter Enhanced SSR Theory

Alharbi¹,

Wang²,

Ge³

2017

JCC

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Low visibility in foggy days results in less contrasted and blurred images with color distortion which adversely affects and leads to the sub-optimal performances in image and video monitoring systems. The causes of foggy image degradation were explained in detail and the approaches of image enhancement and image restoration for defogging were introduced. The study proposed an enhanced and advanced form of the improved Retinex theory-based dehazing algorithm. The proposed algorithm achieved novel in the manner in which the dark channel prior was efficiently combined with the dark-channel prior into a single dehazing framework. The proposed approach performed the first stage in dehazing within the dark channel domain through implementation with an adaptive filter. This novel approach allowed for the dark channel features to be efficiently refined and boosted, a scheme, which according to the obtained results, significantly improved dehazing results in later stages. Experimental results showed that this approach did little to trade-off dehazing speed for efficiency. This makes the proposed algorithm a strong candidate for real-time systems due to its capability to realize efficient dehazing at considerably rapid speeds. Finally, experimental results were provided to validate the superior performance and efficiency of the proposed dehazing algorithm.

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Single Image Haze Removal Using Dark Channel Prior

Cited by 4,438 publications

References 17 publications

Automatic Red-Channel underwater image restoration

Automatic Red-Channel underwater image restoration

Impact of Dehazing on Underwater Marker Detection for Augmented Reality

A Novel Dark-Channel Dehazing Algorithm Based on Adaptive-Filter Enhanced SSR Theory

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