From Blurry to Sharp: AI-Based Satellite Image Resolution Enhancement

Below are the OpenCV DNN AI models for super-resolution that can be utilized to improve the resolution of your satellite images, including their features and download links:

EDSR (Enhanced Deep Residual Networks)

• Scale Factors: 2x, 3x, 4x

• Strengths: Best quality, state-of-the-art results

• Weaknesses: Comparatively large model size (~67MB for 4x)

• URL: python # 4x model

https://github.com/Saafke/EDSR_TensorFlow/raw/master/models/EDSR_x4.pb

ESPCN (Efficient Sub-Pixel CNN)

• Scale Factors: 2x, 3x, 4x

• Strengths: Fastest inference, small model size (~100KB)

• Weaknesses: Lower quality than EDSR

• URL: python 4x model

https://github.com/Saafke/EDSR_TensorFlow/raw/master/models/ESPCN_x4.pb

FSRCNN (Fast Super-Resolution CNN)

• Scale Factors: 2x, 3x, 4x

• Strengths: Good speed/quality balance

• Weaknesses: Limited to smaller upscaling factors

• URL: python 4x model https://github.com/Saafke/EDSR_TensorFlow/raw/master/models/FSRCNN_x4.pb

LapSRN (Laplacian Pyramid Network)

• Scale Factors**: 2x, 4x, 8x (progressive upscaling)

• Strengths**: Progressive upscaling, handles large factors

• Weaknesses**: Slower than ESPCN/FSRCNN

• URL: python 8x model

https://github.com/Saafke/EDSR_TensorFlow/raw/master/models/LapSRN_x8.pb

Implementation Example

import cv2

# Initialize model (example for ESPCN)

sr = cv2.dnn_superres.DnnSuperResImpl_create()

sr.readModel('ESPCN_x4.pb') # Replace with your model file

sr.setModel('espcn', 4) # Change model type and scale

# Model types correspond to filenames:

# - 'edsr' for EDSR_x4.pb

# - 'espcn' for ESPCN_x4.pb

# - 'fsrcnn' for FSRCNN_x4.pb

# - 'lapsrn' for LapSRN_x8.pb

How Deep Learning Enhances Satellite Image Resolution

Performance Comparison

Model	Speed (ms)	PSNR (dB)	Model Size	Best For
EDSR	1200	28.9	67MB	High-quality SR
ESPCN	15	26.4	100KB	Real-time apps
FSRCNN	25	27.1	200KB	Balanced quality
LapSRN	450	27.9	5Mb	Large upscaling

Recommended Use Cases

• Satellite/Aerial Imagery (NAIP): Use EDSR for maximum detail preservation

• Real-Time Processing: Use ESPCN or FSRCNN

• 8× Upscaling: Use LapSRN

  
  

Important Things to Remember

• Ensure that the scale factor in `setModel()` corresponds with the downloaded model (for instance, use `sr.setModel('lapsrn', 8)` for `LapSRN_x8.pb`).

• Models perform optimally with RGB images (3 channels).

Let us know if you need help implementing a specific model! 🛰️

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