Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences

Kim, Hak Gu; Seo, Seung Ji; Song, Byung Cheol

doi:10.1016/j.jvcir.2014.10.006

Cited by 8 publications

(2 citation statements)

References 13 publications

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“…There are also works with less emphasis on using temporal data. Barnum et al [35] modeled rain streaks in frequency space, Santhaseelan and Asari [36] use phase congruency on frame difference images to detect candidate rain streaks, and Kim et al [37] applied horizontal directional thresholding to detect candidate rain streaks.…”

Section: Video-based Methodsmentioning

confidence: 99%

Robust Video Content Alignment and Compensation for Clear Vision Through the Rain

Chen,

Tan,

Hou

et al. 2018

Preprint

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Outdoor vision-based systems suffer from atmospheric turbulences, and rain is one of the worst factors for vision degradation. Current rain removal methods show limitations either for complex dynamic scenes, or under torrential rain with opaque occlusions. We propose a novel derain framework which applies superpixel (SP) segmentation to decompose the scene into depth consistent units. Alignment of scene contents are done at the SP level, which proves to be robust against rain occlusion interferences and fast camera motion. Two alignment output tensors, i.e., optimal temporal match tensor and sorted spatial-temporal match tensor, provide informative clues for the location of rain streaks and the occluded background contents. Different classical and novel methods such as Robust Principle Component Analysis and Convolutional Neural Networks are applied and compared for their respective advantages in efficiently exploiting the rich spatial-temporal features provided by the two tensors. Extensive evaluations show that advantage of up to 5dB is achieved on the scene restoration PSNR over state-of-the-art methods, and the advantage is especially obvious with highly complex and dynamic scenes. Visual evaluations show that the proposed framework is not only able to suppress heavy and opaque occluding rain streaks, but also large semi-transparent regional fluctuations and distortions.

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Section: Video-based Methodsmentioning

confidence: 99%

Robust Video Content Alignment and Compensation for Clear Vision Through the Rain

Chen,

Tan,

Hou

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Then, rain streaks are detected by combining the linear photometric constraint and their dynamic motions. This approach may generate false detections between rain streaks and moving objects (Kim H. G. et al, 2015 ) and the linear photometric constraint is also not valid for DVS platforms. Zhang et al propose to exploit both temporal and chromatic properties of rain streaks to detect them in the video.…”

Section: Related Studymentioning

confidence: 99%

A Novel Rain Removal Approach for Outdoor Dynamic Vision Sensor Event Videos

et al. 2022

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As bio-inspired vision devices, dynamic vision sensors (DVS) are being applied in more and more applications. Unlike normal cameras, pixels in DVS independently respond to the luminance change with asynchronous output spikes. Therefore, removing raindrops and streaks from DVS event videos is a new but challenging task as the conventional deraining methods are no longer applicable. In this article, we propose to perform the deraining process in the width and time (W-T) space. This is motivated by the observation that rain steaks exhibits discontinuity in the width and time directions while background moving objects are usually piecewise smooth along with both directions. The W-T space can fuse the discontinuity in both directions and thus transforms raindrops and streaks to approximately uniform noise that are easy to remove. The non-local means filter is adopted as background object motion has periodic patterns in the W-T space. A repairing method is also designed to restore edge details erased during the deraining process. Experimental results demonstrate that our approach can better remove rain noise than the four existing methods for traditional camera videos. We also study how the event buffer depth and event frame time affect the performance investigate the potential implementation of our approach to classic RGB images. A new real-world database for DVS deraining is also created and shared for public use.

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Selecting Informative Samples for Animal Recognition in the Wildlife

Favorskaya

Buryachenko

2019

Smart Innovation, Systems and Technologies

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Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences

Cited by 8 publications

References 13 publications

Robust Video Content Alignment and Compensation for Clear Vision Through the Rain

Robust Video Content Alignment and Compensation for Clear Vision Through the Rain

A Novel Rain Removal Approach for Outdoor Dynamic Vision Sensor Event Videos

Selecting Informative Samples for Animal Recognition in the Wildlife

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