• Anirudh Raghavendra Rao
• Gurunagasimha S
• H Lakshmish
• Galla Srinivas
• R C Ravindranath

Images that are captured underwater generally suffer from profound categories related to decreased visibility, low brightness, high contrast, high noise and color distortion. Various image processing techniques are used to improve the quality of images, which are often degraded by environmental influences and constraints associated to optical gears. The repair and improvement of underwater images have drawn more and more scientific attention during the last few decades. The survey of underwater images has a crucial problem in the processing of images, is one of the areas that still needs a thorough and in-depth analysis of associated advancements and successes. By examining the contributions and limits of existing approaches through knowledge of underwater image enhancement and restoration, a method has been proposed to improve underwater picture visibility by pre-processing which includes contrast and brightness improvement, denoising and detail recovery. The proposed strategy offers greater overall performance when it comes to the handling of submerged image improvement. The problem of the brightness of enhanced images is solved. The information on texture in the images is properly recovered. Thereby, demonstrating a more robust approach to the problem.

Deep Learning, Underwater Image Enhancement, Feature Fusion, Image Processing, Residual Convolution Neural Network, Attention Mechanism.

"Revealing the Mysteries of the Deep – Underwater Image Enhancement", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 10, page no.e485-e493, October-2023, Available :http://www.jetir.org/papers/JETIR2310373.pdf

"Revealing the Mysteries of the Deep – Underwater Image Enhancement", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 10, page no. ppe485-e493, October-2023, Available at : http://www.jetir.org/papers/JETIR2310373.pdf