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Domain-adaptive Video Deblurring via Test-time Blurring(ECCV 2024)

Jin-Ting He, Fu-Jen Tsai, Jia-Hao Wu, Yan-Tsung Peng, Chung-Chi Tsai, Chia-Wen Lin, Yen-Yu Lin

paper supplement

Abstract : Dynamic scene video deblurring aims to remove undesirable blurry artifacts captured during the exposure process. Although previous video deblurring methods have achieved impressive results, they suffer from significant performance drops due to the domain gap between training and testing videos, especially for those captured in real-world scenarios. To address this issue, we propose a domain adaptation scheme based on a blurring model to achieve test-time fine-tuning for deblurring models in unseen domains. Since blurred and sharp pairs are unavailable for fine-tuning during inference, our scheme can generate domain-adaptive training pairs to calibrate a deblurring model for the target domain. First, a Relative Sharpness Detection Module is proposed to identify relatively sharp regions from the blurry input images and regard them as pseudo-sharp images. Next, we utilize a blurring model to produce blurred images based on the pseudo-sharp images extracted during testing. To synthesize blurred images in compliance with the target data distribution, we propose a Domain-adaptive Blur Condition Generation Module to create domain-specific blur conditions for the blurring model. Finally, the generated pseudo-sharp and blurred pairs are used to fine-tune a deblurring model for better performance. Extensive experimental results demonstrate that our approach can significantly improve state-of-the-art video deblurring methods, providing performance gains of up to 7.54dB on various real-world video deblurring datasets.

Teaser

Pipeline

Pipeline

Installation

conda create -n IDBlau python=3.9
conda activate IDBlau
conda install pytorch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 pytorch-cuda=11.7 -c pytorch -c nvidia
pip install opencv-python tqdm tensorboardX pyiqa thop numpy pandas

Preparation

Training dataset

In our work, we train the ID-Blau, BME and Video Deblurring Model on GoPro dataset.

Testing dataset

We test the following real-world dataset to validate our test-time adaptation method. BSD RBVD RealBlur-j

Model weights

Before doing test-time adaptation for video deblurring model, you need to prepare the model weights for ID-Blau and BME. You can follow ID-Blau and BME to train your own model weight or download our pretrained weights. For each video deblurring model, you can download their pretrained weight(GoPro) from thier github repo.

Dataset Structure

The dataset formats we use follow the BSD dataset format.

dataset/
├── video1/
│ ├── Blur/RGB/
├── video2/
│ ├── Blur/RGB/
├── video3/
│ ├── Blur/RGB/

Implementation

The following section is our test-time adaptation method. We have divided our method into four parts for better maintenance and development.

Step 1: Relative Sharpness Detection Module

In our work, we first detect the relatively sharp patches from testing video. Before running RSDM/main.py, you need to modify lines 108 to 113 to include your data and model weights paths. If you want to know more, you can refer to our paper or RSDM.

python RSDM/main.py

Step 2: Domain-adaptive Blur Condition Generation Module

This module aims to generate the domain-adaptive blur condition for testing video. Before running DBCGM/main.py, you need to modify lines 237 to 248 to include your data and model weights paths.

python DBCGM/main.py

Step 3: Generate domain-specific training data

This section utilize the ID-Blau and our domain-adaptive blur condition to generate new training data for fine-tuning deblurring model. Before running BlurringModel/main.py, you need to modify lines 161 to 164 to include your data and model weights paths.

python BlurringModel/main.py

Step 4: Adapting and Testing the Deblurring Model

Now, you can use the result from our RSDM and the new training data from ID-Blau to fine-tune your video deblurring model. In this section, I take ESTRNN as a example. You can download the GoPro pretrained weight of ESTRNN from weight. (If you want to utilize our method to adapt your own vide blurring model, you can refer our dataloader in ESTRNN.)

Adapting

Before adapting this model, you need to modify line 225 to 229 in deblur_finetune_DDP.py to include your data and model weights path.

CUDA_VISIBLE_DEVICES=0,1 torchrun --nproc_per_node=2 --master_port=29500 DeblurringModel/ESTRNN/deblur_finetune_DDP.py

Testing

Before testing the model adapted by our method, you need to modify line 149 to 154 in deblur_inference.py to include your data and model weights path.

python DeblurringModel/ESTRNN/deblur_inference.py

Results

We compare the deblurring performance of four video deblurring methods using five video deblurring datasets. "Baseline" indicates that the deblurring model is trained on the GoPro dataset and tested on a specific dataset. "+Ours" indicates that the deblurring model is adapted using our method. This table demonstrates that our method can significantly improve the performance of existing video deblurring models on various real-world datasets. Results

Citation

@inproceedings{He2024DADeblur,
  author    = {Jin-Ting He, Fu-Jen Tsai, Jia-Hao Wu, Yan-Tsung Peng, Chung-Chi Tsai, Chia-Wen Lin, Yen-Yu Lin},
  title     = {Domain-adaptive-Video-Deblurring-via-Test-time-Blurring},
  booktitle = {ECCV},
  year      = {2024}
}