NoirInsight

RetroRevive

PPT PRESENTATION

https://docs.google.com/presentation/d/10juHK59FMRPHdhp8T9TkTh-aAFeU1l5bgc6aStm2Pvg/edit?usp=sharing

Working Example

Working.mp4

https://drive.google.com/file/d/1bKmZYZhf2wU8RjcAMGequAcwILHvdu1X/view?usp=sharing

---

In today's digital age, visual content plays a significant role in communication and expression. Our project, RetroRevive, aims to enhance the visual experience by leveraging machine learning to colorize grayscale images and videos, bringing them to life with vibrant hues and tones.

📝 Table of Contents

• Problem Statement
• Idea / Solution
• Future Scope
• Built With
• Collaborators

🧐 Problem Statement

Grayscale images and videos lack the vibrancy and realism of their color counterparts, limiting their visual appeal and effectiveness in conveying information and emotions.

💡 Idea / Solution

We have developed a machine learning model that can automatically colorize grayscale images and videos. This model analyzes the content and context of the grayscale input and intelligently adds color to recreate the scene with accuracy and realism. By integrating this model into our platform, users can effortlessly transform their monochrome visuals into vibrant, colorful creations.

🚀 Future Scope

• Integration with Historical Data: We plan to enhance our model by incorporating historical color data to improve color accuracy and consistency across different scenes and time periods.

• Real-time Colorization: In addition to static images and pre-recorded videos, we aim to develop real-time colorization capabilities, allowing users to see their content come to life with color as they capture it.

• Customization Options: We intend to provide users with customization options, such as adjusting color saturation, brightness, and contrast, to give them greater control over the colorization process and achieve their desired visual effects.

• Collaboration with Creative Professionals: Partnering with artists, photographers, and filmmakers will enable us to gather feedback and insights to further refine and optimize our colorization algorithms for various artistic and professional applications.

⛏️ Built With

• Python
• TensorFlow/Keras (for model development)
• OpenCV (for image and video processing)
• HTML
• CSS
• JavaScript
• Django

Our machine learning model for colorization:

• Convolutional Neural Networks (CNNs)
• Generative Adversarial Networks (GANs)
• Image and video datasets for training and validation

🧑🏼‍💻 Collaborators

Taranjeet Singh Bedi	Devesh Patel	Achintya Gupta	Abhinav Singh	Shaurya Awasthi