opensr-srgan 0.1.38

Modular Super-Resolution GAN Framework for Remote Sensing.

🌍 Single Image Super-Resolution Remote Sensing 'SRGAN'

Remote-Sensing-SRGAN is a research-grade GAN framework for super-resolution of Sentinel-2 and other remote-sensing imagery. It supports arbitrary band counts, configurable generator/discriminator designs, scalable depth/width, and a modular loss system designed for stable GAN training on EO data.

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📖 Documentation

Full docs live at srgan.opensr.eu. They cover usage, configuration, training recipes, and deployment tips in depth.

🧠 Highlights

• Flexible models: swap between SRResNet, RCAB, RRDB, and LKA-style generators with YAML-only changes.
• Remote-sensing aware losses: combine spectral, perceptual, and adversarial objectives with tunable weights.
• Stable training loop: generator pretraining, adversarial ramp-ups, EMA, and multi-GPU Lightning support out of the box.
• PyPI distribution: pip install opensr-srgan for ready-to-use presets or custom configs.
• Extensive Logging: Logging all important information automatically to WandB for optimal insights.

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🏗️ Configuration Examples

All key knobs are exposed via YAML in the opensr_srgan/configs folder:

• Model: in_channels, n_channels, n_blocks, scale, block_type ∈ {SRResNet, res, rcab, rrdb, lka}
• Losses: l1_weight, sam_weight, perceptual_weight, tv_weight, adv_loss_beta
• Training: pretrain_g_only, g_pretrain_steps, adv_loss_ramp_steps, label_smoothing, generator LR warmup (Schedulers.g_warmup_steps, Schedulers.g_warmup_type), discriminator cadence controls
• Data: band order, normalization stats, crop sizes, augmentations

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🎚️ Training Stabilization Strategies

• G‑only pretraining: Train with content/perceptual losses while the adversarial term is held at zero during the first g_pretrain_steps.
• Adversarial ramp‑up: Increase the BCE adversarial weight linearly or smoothly (cosine) over adv_loss_ramp_steps until it reaches adv_loss_beta.
• Generator LR warmup: Ramp the generator optimiser with a cosine or linear schedule for the first 1–5k steps via Schedulers.g_warmup_steps/g_warmup_type before switching to plateau-based reductions.
• EMA smoothing: Enable Training.EMA.enabled to keep a shadow copy of the generator. Decay values in the 0.995–0.9999 range balance responsiveness with stability and are swapped in automatically for validation/inference.

The schedule and ramp make training easier, safer, and more reproducible.

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⚙️ Config‑driven components

Component	Options	Config keys
Generators	SRResNet, res, rcab, rrdb, lka	Generator.model_type, depth via Generator.n_blocks, width via Generator.n_channels, kernels and scale.
Discriminators	standard SRGAN CNN, patchgan	Discriminator.model_type, granularity with Discriminator.n_blocks.
Content losses	L1, Spectral Angle Mapper, VGG19/LPIPS perceptual metrics, Total Variation	Weighted by Training.Losses.* (e.g. l1_weight, sam_weight, perceptual_weight, perceptual_metric, tv_weight).
Adversarial loss	BCE‑with‑logits on real/fake logits	Warmup via Training.pretrain_g_only, ramped by adv_loss_ramp_steps, capped at adv_loss_beta, optional label smoothing.

The YAML keeps the SRGAN flexible: swap architectures or rebalance perceptual vs. spectral fidelity without touching the code.

🧰 Installation

Follow the installation instructions for package, source, and dependency setup options.

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🚀 Quickstart

• Datasets: Dataset structure, downloads, and custom loaders are documented in the data guide.
• Training: Launch training with python -m opensr_srgan.train --config opensr_srgan/configs/config.yaml or follow the training walkthrough.
• Inference: Ready-made presets and large-scene pipelines are described in the inference section.

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🏗️ Configuration & Stabilization

All tunable knobs—architectures, loss weights, schedulers, and EMA—are exposed via YAML files under opensr_srgan/configs. Strategy tips for warm-ups, adversarial ramps, and EMA usage are summarised in the training concepts chapter.

📂 Repository Structure

SISR-RS-SRGAN/
├── opensr_srgan/         # Library + training code
├── docs/                 # MkDocs documentation sources
├── paper/                # Publication, figures, and supporting material
├── pyproject.toml        # Packaging metadata
└── requirements.txt      # Development dependencies

Contribution and Issues

If you wish to contribute (such as new models, data or functionalities), please review the contribution guidelines and open a PR here on GitHub. If you're having problems or need support, please open an Issue here on GitHub.

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📚 Related Projects

• OpenSR-Model – Latent Diffusion SR (LDSR-S2)
• OpenSR-Utils – Large-scale inference & data plumbing
• OpenSR-Test – Benchmarks & metrics
• SEN2NEON – Multispectral HR reference dataset

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✍️ Citation

If you use this work, please cite:

coming soon...

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🧑‍🚀 Authors & Acknowledgements

Developed by Simon Donike (IPL–UV) within the ESA Φ-lab / OpenSR initiative.

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