Image-to-Image Translation

Project Overview:

This project focused on developing advanced deep learning models for Reverse Semantic Segmentation and Super Resolution tasks. The goal was to generate high-quality outputs with enhanced detail and accuracy while ensuring fast training convergence.

Key Achievements:

• Model Implementation: Implemented and fine-tuned models like Pix2Pix, pSp Encoder, and SRGAN to solve challenges in reverse semantic segmentation and super-resolution tasks, delivering visually compelling results.
• Dataset Utilization: Applied models to datasets like Cityscapes for urban segmentation and CelebA-HQ for high-resolution facial imagery, ensuring robust and generalizable outcomes.
• Optimization for Efficiency: Designed workflows to accelerate model convergence and reduce computational overhead, refining loss functions and applying advanced training techniques for improved image quality.

Tools & Technologies:

Deep Learning Frameworks: Leveraged architectures like Pix2Pix, pSp Encoder, and SRGAN for accurate image-to-image translation and upscaling.
Programming Languages: Used C#, .NET, and SQL for backend integration and data processing.
Data Handling: Employed XML for streamlined data representation and evaluation workflows.

Impact:

Produced high-quality super-resolution images and semantically segmented reconstructions with exceptional fidelity. The project demonstrated the potential for adapting state-of-the-art AI techniques to practical applications, contributing to advancements in computer vision and graphics.

Datasets:

Cityscapes: Tackled challenges in urban landscape segmentation for precise modeling of complex environments.
CelebA-HQ: Achieved photorealistic results in high-resolution human imagery for advanced image restoration and enhancement.