Removal of rain from videos: a review

Tripathi, Abhishek Kumar; Mukhopadhyay, Sudipta

doi:10.1007/s11760-012-0373-6

Cited by 89 publications

(32 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…To overcome this limitation, more advanced algorithms have been developed to account for the temporal correlation of rain streaks (Kim et al, ), chromatic properties (X. Zhang et al, ) or shape characteristics (Santhaseelan & Asari, ). Tripathi and Mukhopadhyay () reviewed the video‐based rain‐streak identification algorithms in detail.…”

Section: Methodsmentioning

confidence: 99%

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

Jiang

Babovic

Zheng

et al. 2019

Water Resources Research

View full text Add to dashboard Cite

“Opportunistic sensing” represents an appealing idea for collecting unconventional data with broad spatial coverage and high resolution, but few studies have explored its feasibility in hydrology. This study develops a novel approach to measuring rainfall intensity in real‐world conditions based on videos acquired by ordinary surveillance cameras. The proposed approach employs a convex optimization algorithm to effectively decompose a rainy image into two layers: a pure rain‐streak layer and a rain‐free background layer, where the rain streaks represent the motion blur of falling raindrops. Then, it estimates the instantaneous rainfall intensity via geometrical optics and photographic analyses. We investigated the effectiveness and robustness of our approach through synthetic numerical experiments and field tests. The major findings are as follows. First, the decomposition‐based identification algorithm can effectively recognize rain streaks from complex backgrounds with many disturbances. Compared to existing algorithms that consider only the temporal changes in grayscale between frames, the new algorithm successfully prevents false identifications by considering the intrinsic visual properties of rain streaks. Second, the proposed approach demonstrates satisfactory estimation accuracy and is robust across a wide range of rainfall intensities. The proposed approach has a mean absolute percentage error of 21.8%, which is significantly lower than those of existing approaches reported in the literature even though our approach was applied to a more complicated scene acquired using a lower‐quality device. Overall, the proposed low‐cost, high‐accuracy approach to vision‐based rain gauging significantly enhances the possibility of using existing surveillance camera networks to perform opportunistic hydrology sensing.

show abstract

Section: Methodsmentioning

confidence: 99%

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

Jiang

Babovic

Zheng

et al. 2019

Water Resources Research

View full text Add to dashboard Cite

show abstract

“…Therefore, it is highly desirable to remove raindrops from rainy images to ensure the reliability of outdoor vision systems [1]. For this purpose, numerous efforts have been made in past years.…”

Section: Introductionmentioning

confidence: 99%

“…For this purpose, numerous efforts have been made in past years. One common strategy is using video sequences [1,2] for rain removal. The main idea of this kind of methods is to explore the redundant temporal information from multiple images.…”

Section: Introductionmentioning

confidence: 99%

“…Some single-image-based rain removal methods regard the problem as a layer separation problem. For example, Huang et al [3] attempted to separate the rain streaks from the high-frequency *Correspondence: zhenghaoshimtap@163.com 1 School of Computer Science and Engineering, Xi'an University of Technology, No. 5 Jinhua South Road, 710048 Xi'an, China Full list of author information is available at the end of the article layer by sparse coding, with a learned dictionary from the histogram of oriented gradients (HOG) features.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Weighted median guided filtering method for single image rain removal

Shi

Zhang³

et al. 2018

J Image Video Proc.

View full text Add to dashboard Cite

Because there is no temporal information available, rain removal with a single image is more challenging than that with a video. In this paper, we present a weighted median guided filtering method for rain removal with a single image. It consists of two filtering operations. Firstly, a weighted median filter is convoluted with an input rainy image to obtain a coarse rain-free image; then, guided filter is employed to obtain a refined rain-free image, where the coarse rain-free image is used as a guided image and convoluted with the input rainy image via guided filter. Experimental results show that the proposed method generated comparable results to the state-of-the-art algorithms with low computation cost.

show abstract

“…To date, many research studies have been conducted in order to solve this challenging problem, e.g. [17][18][19][20][21][22][23][24][25][26]. All of them detect raindrops use arithmetic operations for real numbers.…”

Section: Introductionmentioning

confidence: 99%

Measurements of rainfall rates from videos

Dong

Liao

et al. 2017

2017 10th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)

View full text Add to dashboard Cite

Abstract-Measuring rainfall rates from videos is a novel research topic. Due to rain motion, reflection of light and background clutter, it is extremely challenging to obtain accurate measurements. In this paper, we propose a new technique for measuring rainfall rates from videos, which consists of the following technical steps: first, we detect raindrops in an image using gray-tone functions and direction of rain streaks; we then select the focused raindrops, based on two features: average color tensor response and average intensity difference. Afterwards, the size of the raindrops is estimated and a raindrop size distribution (RSD) curve is created according to the use of the RSD in meteorology. Finally, a rainfall rate is obtained by fitting the RSD curve with a Gamma distribution model. In the experiment section presented in this paper, the proposed algorithm is evaluated under different light, moderate and heavy rainy conditions. The measurement results of the proposed algorithm are consistent with those of a can-type rain gauge.

show abstract

Removal of rain from videos: a review

Cited by 89 publications

References 17 publications

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

Weighted median guided filtering method for single image rain removal

Measurements of rainfall rates from videos

Contact Info

Product

Resources

About