Colour Correction and enhancement using hybrid learning model for underwater images
Author(s):
B Esther Rani, Dr. S. Chandra Mohan Reddy
Keywords:
Underwater Image enhancement, Colour correction, Convolution Neural Networks (CNN), Bicubic interpolation.
Abstract
Underwater Images play major role in ocean exploration and resource engineering. Underwater Images suffer from colour castes and look bluish. The Enhancement and Color Correction for underwater images becomes challenging due to attenuation and scattering of light. In this paper, the novel deep learning algorithm along with gamma correction is proposed. In the procedure of enhancing, the texture and structural preservation is more important. In our work, the image enhancement is obtained by using the convolution neural networks (CNN).Our process involves two stages mainly the training and testing stage. During training process, the dataset (UIEB) is collected and their up sampled and resized images are stored in a mat file. Up sampled and resized images are obtained by using Bicubic interpolation. Then CNN layers are created. Finally, the CNN network is trained using the data stored in mat files. After the training process, the test image is given as input to network designed earlier. Then finally the high-resolution image is obtained. This method reduces the loss of textural and structural information when compared to state of art methods.
Article Details
Unique Paper ID: 154053

Publication Volume & Issue: Volume 8, Issue 9

Page(s): 688 - 692
Article Preview & Download


Share This Article

Conference Alert

NCSST-2021

AICTE Sponsored National Conference on Smart Systems and Technologies

Last Date: 25th November 2021

SWEC- Management

LATEST INNOVATION’S AND FUTURE TRENDS IN MANAGEMENT

Last Date: 7th November 2021

Go To Issue



Call For Paper

Volume 8 Issue 4

Last Date 25 September 2021

About Us

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org

Social Media

Google Verified Reviews

Contact Details

Telephone:6351679790
Email: editor@ijirt.org
Website: ijirt.org

Policies