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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.

January 2024
Media & Entertainment Company
4 months
5 Engineers

Overview

Developed a GAN-based solution to enhance image resolution, achieving significant improvements in clarity and detail preservation across diverse image types.

Key Results

4x HigherResolution Improvement
32.5 dBQuality Score (PSNR)
<2 secondsProcessing Time
95%User Satisfaction

The 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.

Our Solution

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

1. Dataset Preparation

Curated a diverse dataset of 50,000+ low and high-resolution image pairs for training, covering various image types and scenarios.

2. GAN Architecture

Designed a custom GAN architecture using ResNet-based generator and PatchGAN discriminator with perceptual loss functions.

3. Training Process

Trained the model on high-performance GPUs for 2 weeks, optimizing for both quality and computational efficiency.

4. Hyperparameter Tuning

Fine-tuned learning rates, batch sizes, and loss function weights to achieve optimal performance across different image types.

Image Super Resolution Using GAN - Image 1

Results & 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

Technologies Used

PythonPyTorchCUDAOpenCVNumPyDockerAWS SageMaker

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.

Future Enhancements

We continue to research advancements in GAN technology, exploring real-time processing capabilities, video super-resolution, and integration with cloud-based content creation platforms.

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