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Dense Haze Removal Using Convolution Neural Network

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Techno-Societal 2020

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Abstract

Pictures caught in murky climate show up low conversely. Debasement in the picture contrast is because of lessening in the light energy reflected from the scene object. In this paper, we propose a picture de-right of passage network which upgrades the perceivability of pictures caught in murky climate. The proposed network comprises of multi-scale convolution channels consolidated by commencement module to extricate the multi-scale highlights. Alongside the multi-scale highlight extraction, we propose a utilization of thick associations with engender learned highlights inside the origin modules. Combinely, the proposed network is planned by joining the standards of both initiation and thick module, along these lines, named as beginning thick organization. To prepare the proposed network for picture de-inception, we utilize primary similitude list metric alongside the L1 misfortune. Existing benchmark information bases are used to assess the favorable to presented network for picture de-right of passage. Exploratory examination shows that the proposed network beats the current methodologies for picture de-preliminaries.

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Author information

Authors and Affiliations

  1. SVERI’s COE, Pandharpur, Maharashtra, India

    Mayuri Dongare & Jyoti Kendule

Authors
  1. Mayuri Dongare
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  2. Jyoti Kendule
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Editors and Affiliations

  1. SVERI’s College of Engineering, Pandharpur, Maharashtra, India

    Prashant M. Pawar

  2. Precision Engineering Center, Bhabha Atomic Research Centre, Mumbai, Maharashtra, India

    R. Balasubramaniam

  3. SVERI’s College of Engineering, Pandharpur, Maharashtra, India

    Babruvahan P. Ronge

  4. SVERI’s College of Engineering, Pandharpur, Maharashtra, India

    Santosh B. Salunkhe

  5. SVERI’s College of Engineering, Pandharpur, Maharashtra, India

    Anup S. Vibhute

  6. SVERI’s College of Engineering, Pandharpur, Maharashtra, India

    Bhuwaneshwari Melinamath

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Dongare, M., Kendule, J. (2021). Dense Haze Removal Using Convolution Neural Network. In: Pawar, P.M., Balasubramaniam, R., Ronge, B.P., Salunkhe, S.B., Vibhute, A.S., Melinamath, B. (eds) Techno-Societal 2020. Springer, Cham. https://doi.org/10.1007/978-3-030-69921-5_55

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  • DOI: https://doi.org/10.1007/978-3-030-69921-5_55

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-69920-8

  • Online ISBN: 978-3-030-69921-5

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