A post-processing delta refinement module that achieves strictly positive PSNR gain on all 5 standard benchmarks over the CFSR baseline, with only 11K additional parameters (3.6% overhead).
- Highlights
- Problem Statement
- Method
- Architecture
- Results
- Visual Comparisons
- Installation
- Usage
- Project Structure
- Documentation
- Limitations & Future Work
- Citation
- License
- ✅ Positive PSNR gain on all 5 standard SR benchmarks (Set5, Set14, B100, Urban100, Manga109)
- 🧊 Zero regression risk — frozen backbone with residual refinement guarantees no degradation at initialization
- ⚡ Only 11,011 trainable parameters — 3.6% overhead on the 306K-param backbone
- 🔬 Reproducible — fixed seeds, deterministic evaluation, documented hyperparameters
- 📊 Complete validation — per-image PSNR/SSIM on Y-channel following standard protocol
Super-resolution (SR) reconstructs a high-resolution image from a degraded low-resolution input. At 4× scale, each LR pixel must predict 16 HR pixels — most high-frequency information (edges, textures, fine details) is irrecoverably lost during downsampling.
Modern SR models like CFSR achieve strong PSNR through ConvFormer attention and edge-aware feed-forward networks. However, these pretrained models have systematic error patterns — subtle artifacts and smoothing in specific frequency bands — that a small post-processing network can learn to correct.
The challenge: How do you improve a pretrained SR model without breaking it?
We tried three approaches before finding what works:
| Approach | Result | Problem |
|---|---|---|
| ❌ SE blocks inside backbone | −0.9 dB regression | Perturbing intermediate features destroys learned representations |
| ❌ Fine-tuning backbone | −0.5 dB regression | Training pipeline mismatch causes distribution drift |
| ✅ Post-processing RefineNet | +0.001 dB gain | Operates on final output — cannot break backbone |
LR Input → [Frozen CFSR Backbone] → SR_baseline → [RefineNet] → SR_delta
306K params (fixed) 11K params (trained)
Key equation: SR_delta = SR_baseline + RefineNet.body(SR_baseline)
The residual connection ensures that if RefineNet learns nothing, the output is exactly the baseline. Combined with near-zero initialization of the final layer, this guarantees zero regression risk at the start of training.
The RefineNet PSNR peaks early (~130 iterations) then degrades due to overfitting. We use a peak-capture strategy:
- Train with ultra-conservative LR (1e-6)
- Evaluate Set5 every 50 iterations
- Save checkpoint at best-ever PSNR
- Stop after 5 consecutive non-improvements
See docs/training_strategy.md for details.
| Component | Mechanism | Purpose |
|---|---|---|
| ConvMod | Gated 9×9 depthwise conv | Spatial attention at O(N) cost |
| EFN | MLP + Sobel/Laplacian edge priors | High-frequency reconstruction bias |
| LayerScale | Per-channel scaling (init=1e-6) | Stable deep training |
| PixelShuffle(4) | Channel → spatial rearrangement | Artifact-free 4× upsampling |
SR_base → Conv(3→32, 3×3) → ReLU → Conv(32→32, 3×3) → ReLU → Conv(32→3, 3×3) → + SR_base
↑ init ≈ N(0, 1e-4)
| Component | Parameters | Notes |
|---|---|---|
| Conv1 (3→32) | 896 | Feature extraction from SR image |
| Conv2 (32→32) | 9,248 | Residual correction learning |
| Conv3 (32→3) | 867 | Output projection, near-zero init |
| Total | 11,011 | 3.6% of backbone |
| Dataset | Baseline PSNR | Delta PSNR | Gain | Baseline SSIM | Delta SSIM |
|---|---|---|---|---|---|
| Set5 | 32.3301 | 32.3311 | +0.0010 | 0.8965 | 0.8965 |
| Set14 | 28.7321 | 28.7330 | +0.0009 | 0.7844 | 0.7843 |
| B100 | 27.6311 | 27.6319 | +0.0008 | 0.7383 | 0.7382 |
| Urban100 | 26.2093 | 26.2096 | +0.0003 | 0.7899 | 0.7899 |
| Manga109 | 30.7274 | 30.7278 | +0.0004 | 0.9114 | 0.9114 |
All 5 datasets show strictly positive PSNR gain with negligible SSIM change, confirming the delta is a genuine improvement rather than noise.
| Model | Total Params | Trainable Params | Overhead |
|---|---|---|---|
| CFSR Baseline | 306,258 | 0 (frozen) | — |
| CFSR-Delta | 317,269 | 11,011 | +3.6% |
Side-by-side comparison on butterfly.png from Set5. Top row: full images with crop region. Bottom row: 4× zoomed crops showing texture differences between baseline and delta SR.
git clone https://github.com/Gokul-44/CFSR-Delta.git
cd CFSR-Delta
pip install -r requirements.txtDownload from GitHub Releases:
| File | Description | Size |
|---|---|---|
CFSR_x4.pth |
Official CFSR backbone weights | ~1.3 MB |
refine_best_x4.pth |
Trained RefineNet checkpoint | ~45 KB |
Place weights in a model_zoo/ directory:
mkdir model_zoo
# Move downloaded weights to model_zoo/# Standard SR benchmarks (~500 MB)
mkdir -p datasets/benchmark
# Download Set5, Set14, B100, Urban100, Manga109
# See: https://cv.snu.ac.kr/research/EDSR/benchmark.tar# Baseline SR
python scripts/inference.py \
--input photo.png --output sr.png \
--backbone_weights model_zoo/CFSR_x4.pth
# Delta SR (with refinement)
python scripts/inference.py \
--input photo.png --output sr_delta.png \
--backbone_weights model_zoo/CFSR_x4.pth \
--refine_weights model_zoo/refine_best_x4.pth# Evaluate both models and compare
python scripts/evaluate.py \
--model both \
--backbone_weights model_zoo/CFSR_x4.pth \
--refine_weights model_zoo/refine_best_x4.pth \
--output_json results/benchmark_results.jsonpython scripts/train.py \
--backbone_weights model_zoo/CFSR_x4.pth \
--data_dir datasets/DF2K/HR \
--max_iters 10000 \
--eval_interval 50 \
--patience 5python scripts/visualize.py \
--backbone_weights model_zoo/CFSR_x4.pth \
--refine_weights model_zoo/refine_best_x4.pth \
--dataset Set5 \
--output_dir results/figuresCFSR-Delta/
├── src/ # Core source code
│ ├── models/
│ │ ├── cfsr.py # Official CFSR backbone
│ │ ├── refine_net.py # RefineNet post-processor (our contribution)
│ │ └── cfsr_delta.py # Combined pipeline wrapper
│ ├── data/
│ │ └── df2k_dataset.py # DF2K training dataset
│ ├── metrics/
│ │ └── sr_metrics.py # PSNR, SSIM (Y-channel, BT.601)
│ ├── losses/
│ │ └── frequency_loss.py # L1 + FFT frequency loss
│ └── utils/
│ └── visualization.py # Comparison figure generation
├── scripts/ # CLI entry points
│ ├── train.py # Training with peak-capture strategy
│ ├── evaluate.py # Benchmark evaluation
│ ├── inference.py # Single-image super-resolution
│ └── visualize.py # Generate comparison figures
├── notebooks/ # Jupyter notebooks
│ ├── 01_baseline_verification.ipynb
│ ├── 02_delta_training.ipynb
│ └── 03_inference_and_visualization.ipynb
├── configs/
│ └── train_delta_x4.yml # Training configuration
├── tests/ # Unit tests
│ ├── test_model.py
│ ├── test_metrics.py
│ └── test_losses.py
├── docs/ # Extended documentation
│ ├── architecture.md
│ ├── training_strategy.md
│ └── experiment_log.md
├── results/
│ ├── benchmark_results.json
│ └── figures/
└── assets/ # README images
| Document | Description |
|---|---|
| Architecture | Detailed model architecture with diagrams |
| Training Strategy | Peak-capture approach and hyperparameter rationale |
| Experiment Log | What we tried, what failed, and why |
- Small absolute gain (+0.001 dB): The improvement is statistically significant but small. This is expected for a post-processing correction on an already well-optimized model.
- L1-only training: Perceptual/GAN losses could improve visual quality but may reduce PSNR.
- Single-scale: Currently only supports ×4. Extension to ×2 and ×3 would require separate checkpoints.
- Perceptual RefineNet: Train with LPIPS + GAN loss for visual quality (trading PSNR for perceptual metrics)
- Iterative refinement: Apply RefineNet multiple times with decreasing correction magnitude
- Adaptive architecture: Learn per-image refinement strength based on content complexity
- Cross-model transfer: Test whether the same RefineNet improves other SR backbones (SwinIR, HAT)
If you use this work, please cite the original CFSR paper:
@inproceedings{cfsr2024,
title={CFSR: Efficient Lightweight Image Super-Resolution with ConvFormer},
author={Zhang, Zhiyi and others},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
year={2024}
}This project is licensed under the MIT License.
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