Optimized design of fast single image dehazing algorithm

Yang, Ling; He, Bian; Feng, Jingyu; Zhao, Lisha; Wang, Hua; Liu, Qing; Wang, Hao; Wang, Huawei; Liao, Jiawen

doi:10.1109/iccwamtip.2017.8301472

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…This method has excellent performance in edge maintenance and halo reduction; nevertheless, the dehazing effect of it is not so outstanding because of the limitation of DC size. Yang et al [13] proposed a edge of alternative method to refine transmission map, which could overcome halo artifacts effectively. Chen et al [14] proposed a dehazing-parameter 2 Journal of Control Science and Engineering adaptive method to adjust the degree of haze removal, and an iteration method based on GF is used to optimize transmission.…”

Section: Introductionmentioning

confidence: 99%

An Improved Defogging Algorithm Based on Dark Color Theory Combined with Self-Adaptive Threshold Mechanism

Fang

2018

Journal of Control Science and Engineering

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Defogging algorithms based on dark channel prior have color shift in light color areas because of inaccurate estimation of transmittance. To resolve this problem, a novel improved image clearness method is proposed. Based on the dark channel prior, the essential causes of color shift are analyzed, with two important factors summarized. Then, transmission map is calculated by using 3 * 3 fixed region, and the restoration module based on self-adaptive threshold mechanism for transmission map is provided. Some experiments are carried out to determine parameters in restoration module to correct the transmission map. According to the corrected transmission map, a transmission restoration algorithm is constructed based on the self-adaptive threshold mechanism to improve the performance of the fog-free image. The experiment results show that this method can resolve the color shift in light color areas effectively and guarantee the overall framework of defogging method based on dark color theory unchanged.

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Section: Introductionmentioning

confidence: 99%