Super-Resolution With Deep Learning For Image Enhancement

  • Satellite Image Analysis
  • Aerial Image Analysis
  • Medical Image Processing
  • Compressed Image
  • Video Enhancement, etc.
  • Nearest Neighbor Interpolation
  • Bilinear Interpolation
  • Bicubic Interpolation
  • Pre-Processing and Feature Extraction
  • Non-Linear Mapping
  • Reconstruction
  • Pre-Processing: Up-scales LR image to desired HR size.
  • Feature Extraction: Extracts a set of feature maps from the up-scaled LR image.
  • Non-Linear Mapping: Maps the feature maps representing LR to HR patches.
  • Reconstruction: Produces the HR image from HR patches.
  • 32 residual blocks with 256 channels
  • pixel-wise L1 loss instead of L2
  • no batch normalization layers to maintain range flexibility
  • scaling factor of 0.1 for residual addition to stabilize training
  • Importing Modules
  • Defining the Parameters
  • Training the Model
  • Obtaining the Result

Conclusion

You can clearly observe a significant improvement in the resolution of images post-application of the Super-Resolution algorithm, making it exceptionally useful for Spacecraft Images, Aerial Images, and Medical Procedures that require highly accurate results.

References

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