In this technical report, we briefly introduce the solution of our team
VIELab-HUST for coded target restoration through atmospheric turbulence in CVPR
2023 UG$^2$+ Track 2.2. In this task, we propose an efficient multi-stage
framework to restore a high quality image from distorted frames. Specifically,
each distorted frame is initially aligned using image registration to suppress
geometric distortion. We subsequently select the sharpest set of registered
frames by employing a frame selection approach based on image sharpness, and
average them to produce an image that is largely free of geometric distortion,
albeit with blurriness. A learning-based deblurring method is then applied to
remove the residual blur in the averaged image. Finally, post-processing
techniques are utilized to further enhance the quality of the output image. Our
framework is capable of handling different kinds of coded target dataset
provided in the final testing phase, and ranked 1st on the final leaderboard.
Our code will be available at this https URL

本文介绍了 VIELab-HUST 团队在 CVPR 2023 UG2+ Track 2.2 中，对经过大气湍流干扰后的编码目标进行图像恢复的高效多阶段框架，并在最终排行榜上获得第一名。

CVPR 2023 UG$^2$+ Challenge Track 2.2：大气湍流下编码目标恢复的第一种解决方案

1st Solution Places for CVPR 2023 UG$^2$+ Challenge Track 2.2-Coded  Target Restoration through Atmospheric Turbulence

Training learning-based deblurring methods demands a tremendous amount of
blurred and sharp image pairs. Unfortunately, existing synthetic datasets are
not realistic enough, and deblurring models trained on them cannot handle real
blurred images effectively. While real datasets have recently been proposed,
they provide limited diversity of scenes and camera settings, and capturing
real datasets for diverse settings is still challenging. To resolve this, this
paper analyzes various factors that introduce differences between real and
synthetic blurred images. To this end, we present RSBlur, a novel dataset with
real blurred images and the corresponding sharp image sequences to enable a
detailed analysis of the difference between real and synthetic blur. With the
dataset, we reveal the effects of different factors in the blur generation
process. Based on the analysis, we also present a novel blur synthesis pipeline
to synthesize more realistic blur. We show that our synthesis pipeline can
improve the deblurring performance on real blurred images.

本文分析了真实模糊图像与合成模糊图像之间的差异性，并提出了 RSBlur 数据集及新颖的模糊合成流程，可以更好的训练和提高真实图像的去模糊性能。