Add runnable baseline SRCNN super-resolution pipeline for DeepLense #109
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Context
Supports DEEPLENSE2 proposal: Unsupervised Super-Resolution and Analysis of Real Lensing Images.
This PR establishes a reproducible baseline for super-resolution experiments on gravitational lensing images.
What Changed
New Files Added:
train_srcnn_minimal.py- Standalone SRCNN training script with:README_baseline.md- Documentation covering:Key Features:
Dummy data generation: Synthetic Einstein ring patterns for testing
Cross-platform compatibility:
What This Enables
What's NOT Done (Intentional)
This PR focuses on infrastructure, not model improvements:
These will come in follow-up PRs aligned with DEEPLENSE2 tasks.
Testing
Basic execution:
Expected output:
outputs/srcnn_deeplense.pthoutputs/srcnn_result.pngVerified on:
Next Steps
Follow-up PRs will:
Note: This PR does not modify existing DeepLense pipelines and is fully additive.
Qualitative comparison on synthetic Einstein-ring lensing images:
Left: Low-resolution input, Middle: SRCNN super-resolved output, Right: High-resolution target.