RAPID UNDERWATER IMAGE ENHANCEMENT FOR IMPROVED VISUAL PERCEPTION USING ML

The purpose of this project is to provide a practical alternative that approximates the underlying solution by learning-based methods. Several models based on deep Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) provide state-of-the art performance in learning, to enhance perceptual image quality from a large collection of paired or unpaired data. Hence, an attempt to address these challenges is done by designing a RAPID UNDERWATER IMAGE ENHANCEMENT model and analyzing its feasibility for real-time applications.

Feautures

1. Enhances the given underwater image into clear image in millisecons
2. uses FUIGAN Model
3. Generates Graphs for Better Visualization
4. Simple UI for using the Model

System Architecture

Model Working

UI

Speical Thanks to

Md Jahidul Islam https://github.com/xahidbuffon/FUnIE-GAN