Aerial imaging has profound applications in Military surveillance, Earthquake assessment, Aerospace, etc. Often due to sudden weather fluctuations, there are variations in visibility. In this paper a CNN based architecture is proposed which removes the haze without much degradation in colour and contrast of the image. The Proposed model is trained on RESIDE (OTS) dataset and provided a 66%, 65%, and 9.15% improvement in PSNR, BRISQUE and Entropy respectively when compared with the best of the available haze removal techniques. Also the model has shown 25% reduction in the computational time making it ideal for Real time applications.


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    Titel :

    CNN based Haze Removal for Aerospace and UAV Applications


    Beteiligte:
    Padmadarsan (Autor:in) / S, Biju K. (Autor:in)


    Erscheinungsdatum :

    22.07.2024


    Format / Umfang :

    3607520 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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