Paper Title
A Novel Image Enhancement Framework for ROVS in the Freshwater Environment

This paper presents a novel deep learning-based framework for enhancing underwater images. The proposed framework follows a two-step approach, beginning with haze removal as a preprocessing step, followed by image enhancement using Water-Net, an advanced deep learning model. According to our comprehensive evaluations using both publicly and privately collected datasets from lakes and rivers, experimental results show the effectiveness of our method for improving the quality of underwater images, particularly in challenging scenarios characterized by high turbidity or limited visibility in freshwater. Our method excels in various tasks including deblurring, contrast enhancement, and color vividness improvement. This adaptability of our proposed method makes it highly suitable for applications in underwater target detection and terrain exploration, specifically in the fresh water environment. Furthermore, our proposed method offers an easy integration framework for existing state-of-the-art techniques, facilitating future advancements in image enhancement needs for under-water related applications. To our knowledge, very few investigations have been dedicated to the enhancement of the quality of underwater images in the freshwater environment. The contribution of our work can fill in this knowledge gap. Keywords - Underwater Image Enhancement, Image Quality Improvement, Reduce Haze, Remotely Operated Vehicles (ROVs)