Papers › Deblurring Low-light Images with Light Streaks

Deblurring Low-light Images with Light Streaks

1 Jun 2014CVPR 2014 6archive 2025-07-28

Zhe Hu, Sunghyun Cho, Jue Wang, Ming-Hsuan Yang

Images taken in low-light conditions with handheld cameras are often blurry due to the required long exposure time. Although significant progress has been made recently on image deblurring, state-of-the-art approaches often fail on low-light images, as these images do not contain a sufficient number of salient features that deblurring methods rely on. On the other hand, light streaks are common phenomena in low-light images that contain rich blur information, but have not been extensively explored in previous approaches. In this work, we propose a new method that utilizes light streaks to help deblur low-light images. We introduce a non-linear blur model that explicitly models light streaks and their underlying light sources, and poses them as constraints for estimating the blur kernel in an optimization framework. Our method also automatically detects useful light streaks in the input image. Experimental results show that our approach obtains good results on challenging real-world examples that no other methods could achieve before.

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DeblurringImage Deblurring

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Deblurring RealBlur-J (trained on GoPro) Hu et al PSNR (sRGB) 26.41 #14 of 15 Archive leaderboard report
Deblurring RealBlur-J (trained on GoPro) Hu et al SSIM (sRGB) 0.803 #14 of 15 Archive leaderboard report
Deblurring RealBlur-R (trained on GoPro) Hu et al PSNR (sRGB) 33.67 #12 of 19 Archive leaderboard report
Deblurring RealBlur-R (trained on GoPro) Hu et al SSIM (sRGB) 0.916 #12 of 19 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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