Deep Submerged Image Enhancement and Restoration Process using CNN

  • et al.
N/ACitations
Citations of this article
2Readers
Mendeley users who have this article in their library.
Get full text

Abstract

In oceanographic studies, underwater imagery plays a vital role. Underwater imaging has some of the advanced applications such as hand-held stereo-cam, fish-pond monitoring, etc. The major sources of quality degradation in most of the underwater imaging processes are scattering and absorption which occurs due to light assimilation. In this paper, we propose a two step-strategy in which the former is the enhancement process and latter is the restoration process. Our unavoidable selective and quantitative appraise uncover that our upgraded pictures and recordings have better accessibility in the dark locales, progressed global and local contrast and better edge sharpness. In order to get rid of image quality impairments, we follow a method which involves only a single image. The major advantage of this method is that it does not require a specialized image-capturing equipment. Moreover, our substantiation gives a better accuracy by deploying Convolutional Neural Network(CNN) algorithm.

Cite

CITATION STYLE

APA

Raveena*, C., Kalaivani, R. S., … Rakshitha, T. R. (2020). Deep Submerged Image Enhancement and Restoration Process using CNN. International Journal of Innovative Technology and Exploring Engineering, 9(10), 112–117. https://doi.org/10.35940/ijitee.j7407.0891020

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free