WaveNeXt (Base)

Wavelet + ConvNeXt generator that translates single-channel Sentinel-1 SAR amplitude into 3-channel Sentinel-2-like optical imagery at 256×256.

Model details

How it works

• Wavelet I/O — a fixed orthonormal 2-level Haar transform replaces the patch-embed stem, and an inverse-Haar head reconstructs the optical image, so the network predicts wavelet sub-bands rather than raw pixels.
• ConvNeXt V2-Base backbone transfers ImageNet-22k features into the data-scarce SAR domain.
• High-frequency discriminator (HF-D) — an adversarial critic on the residual x − gaussian_blur(x) drives coherent fine detail. It is used only during training and adds no inference cost; these weights are the generator alone.

Full architecture and design notes: ARCHITECTURE.md.

Intended uses & limitations

Intended use — research on SAR→optical translation, despeckling, and high-frequency detail synthesis for remote-sensing imagery.

Limitations — trained on a representative 5-scene subset of SEN1-2 (scenes 5, 45, 52, 84, 100); performance on regions, seasons, or sensors outside that distribution is unverified. Outputs are plausible reconstructions, not measurements — do not use for quantitative geophysical analysis. Non-commercial use only.

Usage

ONNX (no PyTorch / transformers)

import numpy as np, onnxruntime as ort
from huggingface_hub import hf_hub_download

onnx_path = hf_hub_download("umpaoflumpia/WaveNeXt", "model.onnx")
sess = ort.InferenceSession(onnx_path, providers=["CPUExecutionProvider"])

sar = np.random.randn(1, 1, 256, 256).astype("float32")   # SAR in [-1, 1]
optical = sess.run(None, {"sar": sar})[0]                  # [1, 3, 256, 256] in [-1, 1]

The batch axis is dynamic ([N,1,256,256]). Swap the provider for CUDAExecutionProvider, TensorrtExecutionProvider, CoreMLExecutionProvider, or DmlExecutionProvider to match your hardware.

PyTorch

import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
from omegaconf import OmegaConf
from src.models.wavenext.gen import WaveNeXtGenerator   # from the source repository

weights = hf_hub_download("umpaoflumpia/WaveNeXt", "generator.safetensors")
cfg = OmegaConf.load("src/models/wavenext/config.yaml")
g = WaveNeXtGenerator(cfg).eval()
g.load_state_dict(load_file(weights))

sar = torch.randn(1, 1, 256, 256)          # SAR in [-1, 1]
with torch.no_grad():
    optical = g(sar)                        # [1, 3, 256, 256] in [-1, 1]

Map either output to display range with (x + 1) / 2.

Training

• Data — SEN1-2, paired Sentinel-1/Sentinel-2 patches; a fixed, representative 5-scene split (5, 45, 52, 84, 100).
• Backbone — ConvNeXt V2-Base, ImageNet-22k pretrained; Haar stem and inverse-Haar head are fixed (non-learnable).
• Objective — LSGAN + feature matching + HF-D adversarial + MS-SSIM + per-band Haar L1
  • LPIPS + focal frequency loss + PatchNCE (no pixel-space L1).
• Schedule — AdamW/Adam (lr 2e-4), bf16 mixed precision, EMA (decay 0.999), 200 epochs with a linear LR decay tail.

Reproduce from the source repository: python -m src.models.wavenext.train.

Evaluation

SEN1-2 held-out validation:

The figure above contrasts the baseline (HF-D disabled) with HF-D on a held-out crop: HF-D recovers coherent high-frequency structure the baseline blurs away.

Acknowledgements

Built on ConvNeXt V2 (Meta AI) and trained on SEN1-2 (TU Munich).

License

CC-BY-NC-4.0 — non-commercial. The weights are derived from ConvNeXt V2 (Meta, CC-BY-NC-4.0) and trained on SEN1-2 (research use); those terms are inherited. Please attribute WaveNeXt, ConvNeXt V2, and SEN1-2 in derivative work.