Open AccessRestoration of degraded underwater images with attenuation coefficients as a clue
Author(s) -
C S Dikshith,
Sameeksha M Vernekar,
Chaitra Desai,
Ujwala Patil,
Uma Mudenagudi
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1187/1/012014
Subject(s) - underwater , attenuation , computer science , transmission (telecommunications) , discriminator , residual , block (permutation group theory) , attenuation coefficient , underwater acoustic communication , image restoration , artificial intelligence , computer vision , image (mathematics) , optics , acoustics , image processing , mathematics , algorithm , physics , geology , telecommunications , detector , geometry , oceanography
Underwater image restoration methods require the knowledge of wideband attenuation coefficients per color channel. In this paper, a Generative adversarial network(GAN) framework is proposed for underwater image restoration using the attenuation coefficients as an input. Attenuation coefficients like atmospheric light and transmission map are also estimated in order to restore an underwater image. For atmospheric light estimation, a combination of Res-net and Dense-net architecture is modelled and making it a learning based Residual-Dense Network. Through literature it is evident that, the transmission map describes about the portion of the light that is not scattered and reaches the camera. Transmission map is generated using the underwater image formation model. Further, these attenuation coefficients are given as an input to the proposed GAN architecture for underwater image restoration. The GAN architecture is having a generator block and a discriminator block. The result is demonstrated on synthetic and real underwater data-set and then compared with state-of-the-art method.