MC-Blur: A Comprehensive Benchmark for Image Deblurring

March 31, 2025 · View on GitHub

Our propsoed MC-Blur Benchmark

We construct a large-scale multi-cause (MC-Blur) dataset for image deblurring. It consists of four blur types: uniform blurs, motion blurs by averaging continuous frames, heavy defocus blurs, and real-world blurs. We collect these images from more than 1000 diverse scenes such as buildings, city scenes, vehicles, natural landscapes, people, animals, and sculptures. MC-Blur Benchmark consits of four different subsets, i.e., Real high-fps based Motion-blurred subset (RHM), large-kernel UHD Motion-blurred subset (UHDM), large-scale heavy defocus blurred subset (LSD), and Real Mixed Blurry Qualitative subset (RMBQ).

Downloads

The images of the dataset can be downloaded from the links below.

Baidu Cloud (How to unzip?)

RHM-250-500-1000 (117G total data) (password:ohzp)
UHDM (278G total data) (password:p78n)
LSD (34G total data) (password:sbtu) (Different from the TCSVT paper, the training set actually has 4,500 sharp–blurry pairs, the test set has 1,100 pairs, and the minimum resolution is 1,800 × 1,200. Please use this as the correct information)
RMBQ (110G total data) (password:nwq8)

Download MC-Blur benchmark from the script, run

python download_data.py

Note: The above script will download all subsets of the MC-Blur. You can use "--data" to select. For example:

python download_data.py --data "UHDM_train_test"

Date	Publication	Title	Abbreviation	Code	Platform
2017	CVPR	Deep multi-scale convolutional neural network for dynamic scene deblurring paper	DeepDeblur	Code	Pytorch
2018	CVPR	Deblurgan: Blind motion deblurring using conditional adversarial networks paper	DeblurGAN	Code	Pytorch
2018	CVPR	Scale-recurrent network for deep image deblurring paper	SRN	Code	Tensorflow
2019	ICCV	DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Better paper	DeblurGAN-v2	Code	Pytorch
2019	CVPR	Deep Stacked Hierarchical Multi-Patch Network for Image Deblurring paper	DMPHN	Code	Pytorch
2020	CVPR	Deblurring by Realistic Blurring paper	DBGAN	Code	Pytorch
2021	CVPR	Multi-Stage Progressive Image Restoration paper	MPRNet	Code	Pytorch
2022	CVPR	Restormer: Efficient Transformer for High-Resolution Image Restoration paper	Restormer	Code	Pytorch
2021	ICCV	Rethinking Coarse-To-Fine Approach in Single Image Deblurring paper	MIMO-UNet	Code	Pytorch

Metrics

Abbreviation	Full-/Non-Reference	Platform	Code
PSNR (Peak Signal-to-Noise Ratio)	Full-Reference
SSIM (Structural Similarity Index Measurement)	Full-Reference	MATLAB	Code
NIQE (Naturalness Image Quality Evaluator)	Non-Reference	MATLAB	Code
SSEQ (No-reference Image Quality Assessment Based on Spatial and Spectral Entropies)	Non-Reference	MATLAB	Code

Results for 250-fps images from RHM Set

Method	PSNR	SSIM	Parameter
DeepDeblur	30.38	0.8766	11.72 M
DeblurGAN	24.89	0.6364	6.07 M
SRN	30.57	0.8799	6.88 M
DeblurGAN-v2	26.99	0.8061	7.84 M
DMPHN	30.42	0.8768	21.69 M
DBGAN	27.89	0.8191	11.59 M
MPRNet	31.52	0.9239	20.13 M
Restormer	30.41	0.9106	26.10 M
MIMO-UNet	32.02	0.9285	6.81 M

Results for 500-fps images from RHM Set

Method	PSNR	SSIM	Parameter
DeepDeblur	31.08	0.8974	11.72 M
DeblurGAN	24.66	0.6748	6.07 M
SRN	31.54	0.9051	6.88 MB
DeblurGAN-v2	27.67	0.8320	7.84 M
DMPHN	31.43	0.9018	21.69 M
DBGAN	28.36	0.8388	11.59 M
MPRNet	32.08	0.9300	20.13 M
Restormer	30.98	0.9160	26.10 M
MIMO-UNet	32.89	0.9398	6.81 M

Results for 1000-fps images from RHM Set

Method	PSNR	SSIM	Parameter
DeepDeblur	32.41	0.8966	11.72 M
DeblurGAN	25.20	0.6535	6.07 M
SRN	32.69	0.0.9016	6.88 M
DeblurGAN-v2	29.81	0.8461	7.84 M
DMPHN	32.41	0.9096	21.69 M
DBGAN	29.66	0.8318	11.59 M
MPRNet	33.36	0.9332	20.13 M
Restormer	32.77	0.9264	26.10 M
MIMO-UNet	33.75	0.9360	6.81 M

Results on UHDM Set

Method	PSNR	SSIM	Parameter
DeepDeblur	22.23	0.6322	11.72 M
DeblurGAN	20.39	0.5568	6.07 M
SRN	22.28	0.6346	6.88 M
DeblurGAN-v2	21.03	0.5839	7.84 M
DMPHN	22.20	0.6378	21.69 M
DBGAN	21.52	0.6025	11.59 M
MPRNet	23.70	0.7472	20.13 M
Restormer	22.39	0.7356	26.10 M
MIMO-UNet	22.97	0.7317	6.81 M

Results on LSD Set

Method	PSNR	SSIM	Parameter
DeepDeblur	20.73	0.7218	11.72 M
DeblurGAN	20.04	0.6335	6.07 M
SRN	21.66	0.7664	6.88 M
DeblurGAN-v2	21.13	0.6964	7.84 M
DMPHN	21.23	0.7519	21.69 M
DBGAN	21.56	0.7536	11.59 M
MPRNet	21.32	0.7897	20.13 M
Restormer	22.35	0.8072	26.10 M
MIMO-UNet	22.56	0.7985	6.81 M

Citation

If you think this work is useful for your research, please cite the following paper.

@inproceedings{zhang2023benchmarking,
  title={MC-Blur: A Comprehensive Benchmark for Image Deblurring},
  author={Zhang, Kaihao and Wang, Tao and Luo, Wenhan and Chen, Boheng and Ren, Wenqi and Stenger, Bjorn and Liu, Wei and Li, Hongdong and Yang Ming-Hsuan},
  booktitle={IEEE Transactions on Circuits and Systems for Video Technology},
  year={2023}
}

License

The MC-Blur dataset is released under CC BY-NC-ND license.