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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    Title :

    CNN based Haze Removal for Aerospace and UAV Applications


    Contributors:
    Padmadarsan (author) / S, Biju K. (author)


    Publication date :

    2024-07-22


    Size :

    3607520 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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