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-# Generative-AI based Spatio-Temporal Fusion for Video Super-Resolution through Up-Scaling and Frame-Interpolation (Ongoing Research)
-## A Novel Approach leveraging Auto-Encoders, LSTM Networks and Maximum Entropy Principle.
+# GenAI-Powered Spatio-Temporal Fusion for Video Super-Resolution
+
+
+
-### Objective of Study:
-+ This is what the Training Data looks like :
-
`[Grey1][Grey2,RGB2][Grey3][Grey4][Grey5,RGB5][Grey6][Grey7][Grey8,RGB8][Grey9][Grey10]`
-+ Each [ ] represents Image from a moment in time.
-+ The model is designed in a way to learn Temporal Dependencies between All Grey Images to be able to Generate `Grey_x` Image at Time x, enhancing Temporal Resolution.
-+ The model is designed in a way to learn Spatial Dependencies between All Grey Images having a RGB counterpart, to Generate a RGB version of Grey_x Image at Time, enhancing Spatial Resolution.
-+ The Model will be used to generate RGB counterparts of All Grey Images, so the synthetically generated dataset through Spatio-Temporal Fusion would look like:
-
`[Grey1,RGB1][Grey2,RGB2][Grey3,RGB3][Grey4,RGB4][Grey5,RGB5][Grey6,RGB6][Grey7,RGB7][Grey8,RGB8][Grey9,RGB9][Grey10,RGB10]`
+Exploring the forefront of generative AI to enhance video quality through advanced spatio-temporal fusion techniques by Upscaling and Frame-Interpolation.
+This is an Approach leveraging Auto-Encoders, LSTM Networks and Maximum Entropy Principle.
-### Kindly [Review Issues](https://github.com/iSiddharth20/Spatio-Temporal-Fusion-in-Remote-Sensing/issues) Section.
+## Introduction
+Developing a novel approach to video super-resolution by harnessing the potential of Auto-Encoders, LSTM Networks, and the Maximum Entropy Principle. The project aims to refine the spatial and temporal resolution of video data, unlocking new possibilities in high-resolution, high-fps, more-color-dense videos and beyond.
-### [Dataset](https://www.kaggle.com/datasets/isiddharth/spatio-temporal-data-of-moon-rise-in-raw-and-tif) is now Available!
+## Research Objective
-### [Click Here](./Documentation/Concept_Presentation.pptx) for Powerpoint Presentaion of Concept.
+The main goals of ptojects are:
+- To learn temporal dependencies among spatially-sparse-temporally-dense greyscale image frames to predict and interpolate new frames, hence, increasing temporal resolution.
+- To learn spatial dependencies through spatially-dense-temporally-sparse sequences that include both greyscale and corresponding RGB image frames to generate colorized versions of greyscale frames, thus, enhancing spatial resolution.
-### High Level Overview of Concept :
+Here's a visual representation of our data transformation:
+- **Current Format**: `[Grey-1] [Grey-2, RGB-2] [Grey-3] [Grey-4] ... [Grey-8, RGB-8] [Grey-9] [Grey-10]`
+- **Post-Processing**: `[RGB-1] [RGB-1.5] [RGB-2] [RGB-2.5] ... [RGB-8.5] [RGB-9] [RGB-9.5] [RGB-10]`
+
+## Resource Links
+
+- 🐞 [Issue Tracker](https://github.com/iSiddharth20/Spatio-Temporal-Fusion-in-Remote-Sensing/issues) - Check out open issues and contribute by addressing them.
+- 🌐 [Dataset Access](https://www.kaggle.com/datasets/isiddharth/spatio-temporal-data-of-moon-rise-in-raw-and-tif) - The dataset is now available on Kaggle. Dive into real-world data!
+- 🔗 [Concept Presentation](./Documentation/Concept_Presentation.pptx) - Gain insights into the concept with the Powerpoint presentation.
+- 📊 [System Overview](./Documentation/System_Diagram.png) - See the system diagram for a high-level understanding of the project.
+
+## Concept Overview
+## Contributions Welcome!
+Your interest in contributing to the project is highly respected. Aiming for collaborative excellence, your insights, code improvements, and innovative ideas are highly appreciated. Make sure to check [Contributing Guidelines](CONTRIBUTING.md) for more information on how you can become an integral part of this project.
+
+## Acknowledgements
+A heartfelt thank you to all contributors and supporters who are on this journey to break new ground in video super-resolution technology.
-## Thank You for Your Amazing Contribution!
+
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