Case study Machine Learning
Image Super Resolution Using GAN
Developed a GAN-based solution to enhance image resolution, achieving significant improvements in clarity and detail preservation across diverse image types.
Explore the project- Client
- Media & Entertainment Company
- Timeline
- 4 months
- Project date
- January 2024
- Team
- 5 Engineers
Reported project results · Specific to the original project scope. See results and context below.
The brief
Project overview
This account is restored from Actinode’s original project portfolio. Results describe that project and are not a guarantee for another engagement. “Ready” or compliance-related terminology describes the stated project scope, not an independent certification.
Original challenge
Low-resolution images often lack the necessary details for certain applications, such as image analysis, medical imaging, or content creation. Our client needed a solution that could generate high-resolution images from their low-resolution counterparts while maintaining realism and preserving essential features for their content creation workflow.
Solution and contribution
We implemented a state-of-the-art GAN-based approach using a generator-discriminator architecture trained simultaneously in a competitive manner. The generator creates high-resolution images while the discriminator evaluates them against real high-resolution images, continuously improving the output quality.
Implementation
How we delivered it
Dataset Preparation
Curated a diverse dataset of 50,000+ low and high-resolution image pairs for training, covering various image types and scenarios.
GAN Architecture
Designed a custom GAN architecture using ResNet-based generator and PatchGAN discriminator with perceptual loss functions.
Training Process
Trained the model on high-performance GPUs for 2 weeks, optimizing for both quality and computational efficiency.
Hyperparameter Tuning
Fine-tuned learning rates, batch sizes, and loss function weights to achieve optimal performance across different image types.
The outcome
Results & business impact
Achieved 4x resolution increase while maintaining visual quality
Successfully preserved fine details and textures in enhanced images
Demonstrated versatility across photographs, medical scans, and satellite imagery
Reduced manual editing time by 70% for content creators
Interpret figures with the original scope and baseline above. Where the account does not specify a measurement window, sample or denominator, do not infer one; discuss the relevant evidence and comparable workload with us before using a result to estimate your project.
Conclusion
The successful implementation of the GAN-based image super-resolution model demonstrates our expertise in applying cutting-edge AI technologies to solve real-world challenges. This project not only enhanced our client's image processing capabilities but also opened new possibilities for industries relying on high-quality visual data.
Next steps in the original project account
We continue to research advancements in GAN technology, exploring real-time processing capabilities, video super-resolution, and integration with cloud-based content creation platforms.
Technology & expertise
- Python
- PyTorch
- CUDA
- OpenCV
- NumPy
- Docker
- AWS SageMaker
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