CallMeDaniel/NTIRE2026-InfraredSR

NTIRE2026 Infrared Super-Resolution Models

Pre-trained models for the NTIRE2026 Infrared Image Super-Resolution (x4) Challenge.

Competition

Item	Detail
Task	Single-image super-resolution (x4) for infrared remote sensing
Metric	Score = PSNR + 20 x SSIM (intensity channel, 4px border shave)
Dataset	919 train, 52 val, 222 test infrared images

Available Models

Model	File	Score	PSNR	SSIM	Params	Architecture
RFRSR v10 (split)	rfrsr_v10_split_iter46k.pth	51.57	33.88	0.8822	2.05M	Recurrent Feature Refinement
RFRSR v2	rfrsr_v2_iter250.pth	27.59	15.89	0.5806	2.05M	Recurrent Feature Refinement
MambaOutRS v12	mambaoutrs_v12_iter500.pth	27.52	16.03	0.5811	2.96M	Gated CNN + Fourier Filter Gate
MambaOutRS v10	mambaoutrs_v10_iter500.pth	25.64	14.81	0.5372	2.96M	Gated CNN + Fourier Filter Gate
HSRMamba	hsrmamba_iter20k.pth	25.56	14.74	0.5365	2.4M	Context-SSM + Spectral Reordering

Usage

import torch
from PIL import Image
import numpy as np

# Load model (example with RFRSR)
checkpoint = torch.load("rfrsr_v10_split_iter46k.pth", map_location="cpu")

# For full inference pipeline with TTA and post-processing:
# See https://github.com/danghoangnhan/NTIRE2026

Full Inference with Submission Creator

git clone https://github.com/danghoangnhan/NTIRE2026.git
cd NTIRE2026
pip install -e .

python src/create_submission.py \
  --input-dir data/test_LR_X4/X4 \
  --output-dir output/ \
  --weights-path rfrsr_v10_split_iter46k.pth \
  --arch-config src/options/train/train_rfr_sr_x4_v10_split.yml \
  --tta

Architecture Details

RFRSR (Best)

• 3-iteration recurrent feature refinement loop
• 6 residual blocks, 48 channels per stage
• PixelShuffle 4x upsampling
• Only 2.05M parameters

MambaOutRS

• Gated CNN with Fourier Filter Gate (no SSM)
• 4-stage [6,6,6,6] block design, 48 embedding dims
• 2.96M parameters

HSRMamba

• Context Selective State Space Model
• Global Spectral Reordering for token reorganization
• Uniquely stable (improves past 20k iterations)
• 2.4M parameters

Key Findings

1. Smaller models win: 2.05M RFRSR > 9.5M MiM-ISTD
2. SSIM in loss is critical: Prevents catastrophic training collapse
3. Models peak early (250-1000 iters), then degrade
4. TTA adds +0.1-0.3 dB for free at test time

Training

All models trained with:

• Loss: Charbonnier + SSIM + Gradient + FFT + SWT (IRSRCombinedLoss)
• Optimizer: Adam
• Framework: BasicSR
• Input: Grayscale infrared images (force_gray: true)

License

MIT

Citation

@misc{ntire2026_infraredsr,
  title={NTIRE2026 Infrared Super-Resolution: A Study of Architectures and Training Strategies},
  author={Daniel Ho},
  year={2026},
  url={https://github.com/danghoangnhan/NTIRE2026}
}