• Mrs.D.Suganya
• E.Abishek
• V.Lakshmi Narayanan
• J.Ramachandran
• J. Yasar Arafat

Deep learning is a subset of machine learning that involves neural networks with three or more layers, aiming to mimic the human brain's behavior and learn from vast datasets. While a single-layer neural network can make approximate predictions, additional hidden layers refine accuracy. Deep learning focuses on direct classification tasks from data like images, text, or sound, achieving state-of-the-art accuracy, often surpassing human-level performance. Models are trained on labeled data using complex neural network architectures. The proposed work targets enhancing underwater image quality for various applications like infrastructure inspection, object detection, and marine biology research. It introduces the UNDER WATER Long Short-Term Memory (UWLSTM) network to remove backscattering artifacts, correct color shifts, and reduce haze. Compared to the existing UWGAN, the UWLSTM captures long-term dependencies better, prioritizes critical information, and aims for superior performance in underwater image enhancement. This approach addresses unique underwater challenges effectively, providing a robust solution for improving image quality and usability in diverse underwater applications.

SIMULTANEOUS COLOUR AND BLUR CORRECTION IN UNDERWATER IMAGES USING JOIN DEEP LEARNING

"SIMULTANEOUS COLOUR AND BLUR CORRECTION IN UNDERWATER IMAGES USING JOIN DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 7, page no.g904-g910, July-2021, Available :http://www.jetir.org/papers/JETIR2107840.pdf

"SIMULTANEOUS COLOUR AND BLUR CORRECTION IN UNDERWATER IMAGES USING JOIN DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 7, page no. ppg904-g910, July-2021, Available at : http://www.jetir.org/papers/JETIR2107840.pdf