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An efficient single image haze removal algorithm for computer vision applications

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Abstract

Atmospheric conditions induced by suspended particles such as fog, smog, rain, haze etc., severely affect the scene appearance and computer vision applications. In general, existing defogging algorithms use various constraints for fog removal. The efficiency of these algorithms depends on the accurate estimation of the depth models and the perfection of these models solely relies on pre-calculated coefficients through the training data. However, the depth model developed on the basis of these pre-calculated coefficients for dehazing may provide better accuracy for some kind of images but not equally well for every type of images. Therefore, training data-independent based depth model is required for a perfect haze removal algorithm. In this paper, an effective haze removal algorithm is reported for removing fog or haze from a single image. The proposed algorithm utilizes the atmospheric scattering model in fog removal. Apart from this, linearity in the depth model is achieved by the ratio of difference and sum of the intensity and saturation values of the input image. Besides, the proposed method also take care the well-known problems of edge preservation, white region handling and colour fidelity. Experimental results show that the proposed model is more efficient in comparison to the existing haze removal algorithms in terms of qualitative and quantitative analysis.

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Authors and Affiliations

  1. Jaypee University of Engineering and Technology, Raghogarh, Guna, Madhya Pradesh, 473226, India

    Gaurav Saxena

  2. Madhav Institute of Technology and Science, Gole ka Mandir, Gwalior, Madhya Pradesh, 474005, India

    Sarita Singh Bhadauria

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  1. Gaurav Saxena
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  2. Sarita Singh Bhadauria
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Correspondence to Gaurav Saxena.

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Saxena, G., Bhadauria, S.S. An efficient single image haze removal algorithm for computer vision applications. Multimed Tools Appl 79, 28239–28263 (2020). https://doi.org/10.1007/s11042-020-09421-4

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  • DOI: https://doi.org/10.1007/s11042-020-09421-4

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