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MFIR - Multi-Frame Image Restoration

Name: MFIR
Availability: InStock

A PyTorch model for multi-frame image restoration through temporal fusion and feature-level alignment. MFIR aligns and fuses features from multiple degraded frames to produce a high-quality restored image.

Model Description

MFIR takes 2-16 degraded frames of the same scene and combines them into a single high-quality output. Unlike single-image restoration methods that struggle with heavily degraded inputs, MFIR leverages complementary information across multiple frames - each frame captures slightly different details, and the model learns to extract and merge the best parts from each.

Architecture

Input Frames (B, N, 3, H, W)
         │
         ▼
┌─────────────────────┐
│   Shared Encoder    │  ResNet-style feature extraction
└─────────────────────┘
         │
         ▼
┌─────────────────────┐
│     Deformable      │  Align frames using learned offsets
│     Alignment       │  (3-layer cascade)
└─────────────────────┘
         │
         ▼
┌─────────────────────┐
│  Temporal Attention │  Multi-head attention fusion
│      Fusion         │  (4 heads)
└─────────────────────┘
         │
         ▼
┌─────────────────────┐
│      Decoder        │  PixelShuffle upsampling
└─────────────────────┘
         │
         ▼
   Output (B, 3, H, W)

Key Components

Component	Description
Shared Encoder	Multi-scale feature extraction with residual blocks. 4x spatial downsampling.
Deformable Alignment	Cascaded deformable convolutions (3 layers) to align frames to reference. More robust than optical flow for degraded inputs.
Temporal Attention Fusion	Multi-head attention (4 heads) where reference frame is query, all frames are key/value. Learns per-pixel frame contributions.
Decoder	Progressive upsampling with PixelShuffle (2 stages, 4x total).

Usage

Installation

pip install torch torchvision huggingface_hub

Inference

python

import torch
from huggingface_hub import hf_hub_download

# Download checkpoint
checkpoint_path = hf_hub_download(
    repo_id="marduk-ra/MFIR",
    filename="temporal_fusion_model.pth"
)

# Load model
ckpt = torch.load(checkpoint_path, map_location="cuda", weights_only=False)

# Model architecture code available at:
# https://github.com/marduk-ra/MFIR

from model import FeatureFusionModel, FeatureFusionConfig

config = FeatureFusionConfig.from_dict(ckpt["config"])
model = FeatureFusionModel(config)
model.load_state_dict(ckpt["state_dict"])
model.eval()

# Inference
# frames: (batch, num_frames, 3, height, width) tensor in [0, 1]
with torch.no_grad():
    result = model(frames, ref_idx=0)
    output = result["output"]  # (batch, 3, height, width)

Web Demo

Try the model directly in your browser:

🚀 Hugging Face Space Demo

Model Details

Parameter	Value
Input Channels	3 (RGB)
Output Channels	3 (RGB)
Max Frames	16
Min Frames	2
Encoder Channels	[64, 128, 256]
Deformable Groups	8
Deformable Layers	3
Attention Heads	4
Fusion Type	Attention
Parameters	~10M
Checkpoint Size	42 MB

Example

Input Frames (5 degraded images):

Output (restored):

5 degraded input frames are fused into a single high-quality output.

The model works best when:

Frames have slight variations (different noise patterns, blur, etc.)
Frames are roughly aligned (same scene)
Input resolution matches training resolution

Training

The model was trained on a custom dataset with the following specifications:

Dataset:

16,000 high-resolution source images
Each image was used to generate 8 degraded input frames
Multi-scale training: 128, 256, 512, and 1024 pixel resolutions

Degradation Pipeline:

Random spatial shifts (simulating camera shake)
Motion blur with varying kernel sizes and directions
Gaussian and Poisson noise with random intensity

Training Configuration:

Total epochs: 150 (progressive training)
Optimizer: AdamW
Loss: L1 + Perceptual (VGG) + SSIM + Color Correction

Limitations

Requires multiple frames of the same scene
Performance depends on frame quality variation
GPU recommended for real-time processing

Citation

bibtex

@software{karaarslan2026mfir,
  author = {Karaarslan, Veli},
  title = {MFIR: Multi-Frame Image Restoration},
  year = {2026},
  url = {https://github.com/allcodernet/MFIR}
}

License

MIT License - see LICENSE

Author

Veli Karaarslan - 2026

marduk-ra/
MFIR