An Intelligent Approach of Underwater Image Co-Enhancement Using Correlation Feature Matching with Enhanced Meta-Heuristic Optimization-Aided Transformer UNet

An Intelligent Approach of Underwater Image Co-Enhancement Using Correlation Feature Matching with Enhanced Meta-Heuristic Optimization-Aided Transformer UNet P. Ajanya Department of Computer Science and Engineering, Vels Institute of Science Technology, and Advanced Studies (VISTAS), Krishnapuram, Pallavaram, Chennai 600117, Tamil Nadu, India https://orcid.org/0009-0000-1135-1373 S. Meera Department of Computer Science and Engineering, Vels Institute of Science Technology, and Advanced Studies (VISTAS), Krishnapuram, Pallavaram, Chennai 600117, Tamil Nadu, India https://orcid.org/0000-0002-7964-7012

In marine ecosystems, the geological formation, and aquatic life are analyzed by underwater imaging. The visibility of the aquatic environment is enhanced by the underwater image improvement approaches. Because of the dispersal of the particles, attenuation of light, and distortion of color, the capturing of marine images is a challenging task. The enhanced images with the absence of water turbidity and light attenuation provide an excellent view of the underwater sceneries. The dispersion and incorporation of light underwater cause color distortion effects. Without disturbing the accuracy of the underwater image, the process of obtaining an accurate color representation becomes a complicated task. Overall excellence of the enhanced images may affected by the noise or the artifacts by traditional approaches which tends to affect the image quality. Also, the preservation of important details in the image is a rigorous task while removing the distortion. To combat such issues, an adaptive deep learning-based underwater image co-enhancement is suggested. In the first phase, the images are gathered from the standard dataset. Further, the image is fed into the correlation feature matching module. Here, the feature matching is executed via the Adaptive Trans-Unet (ATUNet), and the correlation feature matching performance of the ATUNet is strengthened by tuning the parameter using the Updated Random Parameter-based Red Kite Optimization Algorithm (URP-RKOA). The usefulness of the developed model is defined using the experimental study. Throughout the analysis, the numerical findings of the developed model attain the values of 155.01, 26.2, and 0.78 in terms of MSE, PSNR, and SSIM. The underwater image co-enhancement achieved better performance rather than existing approaches.
12 16 2024 2750010 10.1142/S0219467827500100 10.1142/S0219467827500100 https://www.worldscientific.com/doi/10.1142/S0219467827500100 https://www.worldscientific.com/doi/pdf/10.1142/S0219467827500100