MC-Blur: A Comprehensive Benchmark for Image Deblurring
- URL: http://arxiv.org/abs/2112.00234v3
- Date: Mon, 11 Sep 2023 10:13:21 GMT
- Title: MC-Blur: A Comprehensive Benchmark for Image Deblurring
- Authors: Kaihao Zhang, Tao Wang, Wenhan Luo, Boheng Chen, Wenqi Ren, Bjorn
Stenger, Wei Liu, Hongdong Li, Ming-Hsuan Yang
- Abstract summary: In most real-world images, blur is caused by different factors, e.g., motion and defocus.
We construct a new large-scale multi-cause image deblurring dataset (called MC-Blur)
Based on the MC-Blur dataset, we conduct extensive benchmarking studies to compare SOTA methods in different scenarios.
- Score: 127.6301230023318
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Blur artifacts can seriously degrade the visual quality of images, and
numerous deblurring methods have been proposed for specific scenarios. However,
in most real-world images, blur is caused by different factors, e.g., motion
and defocus. In this paper, we address how different deblurring methods perform
in the case of multiple types of blur. For in-depth performance evaluation, we
construct a new large-scale multi-cause image deblurring dataset (called
MC-Blur), including real-world and synthesized blurry images with mixed factors
of blurs. The images in the proposed MC-Blur dataset are collected using
different techniques: averaging sharp images captured by a 1000-fps high-speed
camera, convolving Ultra-High-Definition (UHD) sharp images with large-size
kernels, adding defocus to images, and real-world blurry images captured by
various camera models. Based on the MC-Blur dataset, we conduct extensive
benchmarking studies to compare SOTA methods in different scenarios, analyze
their efficiency, and investigate the built dataset's capacity. These
benchmarking results provide a comprehensive overview of the advantages and
limitations of current deblurring methods, and reveal the advances of our
dataset.
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