Colour Correction and enhancement using hybrid learning model for underwater images
B Esther Rani, Dr. S. Chandra Mohan Reddy
Underwater Image enhancement, Colour correction, Convolution Neural Networks (CNN), Bicubic interpolation.
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
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