Multi-Scale Attention Super-Resolution GAN

Transforming Low-Resolution Satellite Imagery for Agricultural Intelligence

4x Resolution Boost
256×256 Output Quality
NDVI Analysis Ready
Try Live Demo

About the Project

Deep Learning Architecture

MSARGAN employs a sophisticated Generative Adversarial Network with multi-scale attention mechanisms, residual blocks, and perceptual loss for superior image quality.

Agricultural Monitoring

Designed specifically for agricultural applications, enabling precise crop health assessment through enhanced satellite imagery and NDVI calculations.

Measurable Performance

Delivers quantifiable improvements with PSNR and SSIM metrics, ensuring reliable image enhancement for critical agricultural decisions.

Live Demo

Upload a 64×64 satellite image to see the AI enhancement in action

Upload Satellite Image

Click to select or drag & drop a 64×64 image

Supports: JPG, PNG (Max: 16MB)

How It Works

1

Image Input

Low-resolution 64×64 satellite image is received as input

2

Feature Extraction

Deep convolutional layers extract spatial features and patterns

3

Residual Processing

8 residual blocks enhance features while preserving details

4

Upsampling

Pixel shuffle layers progressively increase resolution 4x

5

Quality Metrics

PSNR and SSIM calculated to measure enhancement quality

6

NDVI Calculation

Vegetation index computed for agricultural analysis

Technical Architecture

Generator Network

  • 8 Residual Blocks
  • PReLU Activation
  • Batch Normalization
  • Pixel Shuffle Upsampling

Training Strategy

  • Adversarial Loss
  • Perceptual Loss (VGG19)
  • Pixel-wise MSE Loss
  • Adam Optimizer

Performance Metrics

  • PSNR (Peak Signal-to-Noise Ratio)
  • SSIM (Structural Similarity)
  • NDVI (Vegetation Index)
  • Real-time Processing