A total variation and group sparsity based tensor optimization model for video rain streak removal

Wang, Ye-Tao; Zhao, Xi-Le; Jiang, Tai-Xiang; Deng, Liang-Jian; Ma, Tianhui; Zhang, Yue-Tian; Huang, Ting‐Zhu

doi:10.1016/j.image.2018.11.008

Cited by 18 publications

(6 citation statements)

References 19 publications

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“…As shown in Figure 1l, most of the 2 -norm values of columns are close to zero, while the other values are much bigger than them. This explains again that the joint sparsity regularization is more appropriate to depict the structural property of stripes than the normal group sparsity [47] regularization.…”

Section: The Proposed Modelmentioning

confidence: 96%

Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Sun

Huang

et al. 2019

Remote Sensing

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Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to exploit the stripe properties adequately, leading to suboptimal performance. Based on a full consideration of the stripe properties, we propose a new destriping model to achieve stripe detection and stripe removal simultaneously. In this model, we adopt the unidirectional total variation regularization to depict the directional property of stripes and the weighted ℓ 2 , 1 -norm regularization to depict the joint sparsity of stripes. Then, we combine the alternating direction method of multipliers and iterative support detection to solve the proposed model effectively. Comparison results on simulated and real data suggest that the proposed method can remove and detect stripes effectively while preserving image edges and details.

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Section: The Proposed Modelmentioning

confidence: 96%

Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Sun

Huang

et al. 2019

Remote Sensing

Self Cite

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“…This results in the appearance of rain components in the output data. Wang et al [26] developed a group sparsity based optimisation model for rain removal from videos. However, this method fails when the rain direction is far away from vertical direction.…”

Section: Introductionmentioning

confidence: 99%

Tensor total variation regularised low‐rank approximation framework for video deraining

2020

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“…This method is not suitable for removing heavy rain patterns. Wang et al formulated group sparsity based optimization model for rain removal from videos [19]. This method is limited when the rain direction is far away from vertical direction.…”

Section: Introductionmentioning

confidence: 99%

An Automated Unified Framework for Video Deraining and Simultaneous Moving Object Detection in Surveillance Environments

Baiju

George

2020

IEEE Access

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In many instances, outdoor surveillance systems suffer from atrocious weather conditions such as rain, since images or videos captured by such vision systems in rainy days may undergo severe visual dilapidations. This can cause a glitch in those algorithms which are further used for object detection and tracking. Therefore, an ancillary video processing algorithm namely, video deraining is necessary prior to the implementation of object detection and tracking. This indicates the requirement of a time-consuming and complicated two-step process for the detection of moving objects in a rainy environment. This paper proposes an automated single-stage formulation for the conventional three-stage procedure such as rain steak removal, original data recovery and moving object detection as simultaneous operation in the tensor framework. The brilliance of this work is confined in the efficient formulation of an operator termed as Slice Rotational Total Variation (SRTV), to unify the different rain patterns into a common pattern so that any form of rain pattern can be effectively removed from the rainy data in a visually appealing manner by preserving the important details of the data. In this paper, SRTV regularization and tensor low-rank minimization are utilized for the effective deraining as well as efficient retrieval of clean background. Besides, l 1 norm and Tensor Total Variation (TTV) regularizers together with SRTV regularizer are employed for the faithful detection of derained moving objects. The experimental results show that the proposed method outperforms the state of the art methods in terms of deraining capability and accurate moving object detection.

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A total variation and group sparsity based tensor optimization model for video rain streak removal

Cited by 18 publications

References 19 publications

Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Remote Sensing Image Stripe Detecting and Destriping Using the Joint Sparsity Constraint with Iterative Support Detection

Tensor total variation regularised low‐rank approximation framework for video deraining

An Automated Unified Framework for Video Deraining and Simultaneous Moving Object Detection in Surveillance Environments

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