Remote Sensing and Natural Image Dehazing using DCP based IDERS Framework

Shivakumar, Nakka; Kumar, Naluguru Udaya; Bachu, Srinivas; Kumar, M. Aravind

doi:10.1109/iceeict53079.2022.9768593

Cited by 2 publications

(1 citation statement)

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“…For example, Fattal proposed the method of using a Markov model to calculate color information [ 1 ], He et al proposed the dark channel prior (DCP) method [ 2 ], Ling et al proposed the estimation method for perceptual transmission [ 3 ], and Berman et al proposed a nonlocal defogging algorithm using fog line to estimate the transmittance map [ 4 ]. At present, the DCP algorithm and its series of improved algorithms are still the main methods for haze removal in a single image [ 5 , 6 , 7 , 8 , 9 , 10 ]. The common approach of this type is to remove haze by solving or estimating the atmospheric light coefficient value and the atmospheric transmittance rate with the physical model of atmospheric scattering from different aspects.…”

Section: Introductionmentioning

confidence: 99%

Image Haze Removal Method Based on Histogram Gradient Feature Guidance

Huang

Zhang

2023

IJERPH

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Optical remote sensing images obtained in haze weather not only have poor quality, but also have the characteristics of gray color, blurred details and low contrast, which seriously affect their visual effect and applications. Therefore, improving the image clarity, reducing the impact of haze and obtaining more valuable information have become the important aims of remote sensing image preprocessing. Based on the characteristics of haze images, combined with the earlier dark channel method and guided filtering theory, this paper proposed a new image haze removal method based on histogram gradient feature guidance (HGFG). In this method, the multidirectional gradient features are obtained, the atmospheric transmittance map is modified using the principle of guided filtering, and the adaptive regularization parameters are designed to achieve the image haze removal. Different types of image data were used to verify the experiment. The experimental result images have high definition and contrast, and maintain significant details and color fidelity. This shows that the new method has a strong ability to remove haze, abundant detail information, wide adaptability and high application value.

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