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2021 | OriginalPaper | Chapter

Enhancement of Dehazing Methods Using DNN and Filtering

Authors : Baby Naz, Nafisur Rahman, Md. Tabrez Nafis

Published in: Computer Networks and Inventive Communication Technologies

Publisher: Springer Nature Singapore

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Abstract

Murkiness in pictures is because of common ecological wonders, which makes the picture in a white shade commotion. Fog evacuation is one of the most significant research subjects nowadays to the due prevalence of utilizations progressively reconnaissance from rambles or any region under security. Both indoor and outside pictures are significant for testing cloudiness and its expulsion. Many picture-handling methods are made by analysts to evacuate cloudiness in a solitary picture. Murkiness force can be determined by a parameter known as perceptual fog density measure (PFD). It is critical to investigate this parameter for all the strategies in order to get a thought of progress. In this postulation, another methodology is made by applying an all-inclusive guided sifting procedure with a profound neural system. This proposed calculation is executed on MATLAB programming, and results are gotten by figuring the PFD in the current and proposed method. The four methods are compared with one another. The methods are global image filtering (GIF), weighted global image filtering (WGIF), globally guided image filtering (GGIF), and a proposed strategy, for example, comprehensively guided sifting with deep neural network (DNN). In GIF, the fine structure of the picture is commonly not safeguarded and an unreasonable picture is acquired. In WGIF, the PFD got is most noteworthy. In GGIF, PFD is lower and structure is not safeguarded, yet in the proposed calculation, the PDF is least with fine structure, and shading power of the image is of the best quality.

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Metadata
Title
Enhancement of Dehazing Methods Using DNN and Filtering
Authors
Baby Naz
Nafisur Rahman
Md. Tabrez Nafis
Copyright Year
2021
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-15-9647-6_42