A directional global sparse model for single image rain removal

Deng, Liang-Jian; Huang, Ting‐Zhu; Zhao, Xi-Le; Jiang, Tai-Xiang

doi:10.1016/j.apm.2018.03.001

Cited by 134 publications

(101 citation statements)

References 23 publications

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“…As shown in Figure , the identification algorithm is robust when the rain streaks are within ±15° from vertical. This tolerance range is consistent with a previous finding (Deng et al, ) and implies that the effectiveness of employing the Y‐direction total variance (i.e., the vertical smoothness of rain streaks) is not significantly impaired when the angle of rain streaks is within a certain range. One possible reason is that the Y direction total variance regularization term (the second term) merely controls part of the objective function equation and is insensitive to identification as discussed in section .…”

Section: The Effectiveness and Efficiency Of Rain‐streak Identificationsupporting

confidence: 92%

“…In contrast, the single‐image‐based algorithms rely more on the properties of rain streaks (Deng et al, ), making such algorithms more robust for complex applications (e.g., those with nonstatic backgrounds with moving pedestrians or vehicles). The main idea behind single image‐based algorithms is to decompose a rain‐contained image into a rain‐free layer and a rain‐streak layer through photographic analyses of rain streaks (Deng et al, ; Jiang et al, ; Li et al, ). In general, a grayscale rain‐containing image can be mathematically characterized by a two‐dimensional matrix

scriptO \in ℝ^{m \times n}

(where m and n are the image height and width, respectively) whose elements are grayscale values.…”

Section: Methodsmentioning

confidence: 99%

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

Jiang

Babovic

Zheng

et al. 2019

Water Resources Research

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“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.

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Section: The Effectiveness and Efficiency Of Rain‐streak Identificationsupporting

confidence: 92%

scriptO \in ℝ^{m \times n}

(where m and n are the image height and width, respectively) whose elements are grayscale values.…”

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

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“…However, the effects of the rain streaks on the vertical gradient and horizontal gradient are different. This phenomenon was likewise noticed in [19][20][21]. Initially, for the sake of convenience, we assume that rain streaks are approximately vertical.…”

Section: Introductionmentioning

confidence: 59%

“…Zhu et al [16] proposed a joint bi-layer optimization method progressively separate rain streaks from background details, in which the gradient statistics are analyzed. Meanwhile, the directional property of rain streaks received a lot of attention in [19][20][21] and these methods achieved promising performances. Ren et al [23] removed the rain streaks from the image recovery perspective.…”

Section: Introductionmentioning

confidence: 99%

FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors

Jiang

Huang

Zhao

et al. 2019

IEEE Trans. on Image Process.

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141

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Rain streaks removal is an important issue in outdoor vision systems and has recently been investigated extensively. In this paper, we propose a novel video rain streak removal approach FastDeRain, which fully considers the discriminative characteristics of rain streaks and the clean video in the gradient domain. Specifically, on the one hand, rain streaks are sparse and smooth along the direction of the raindrops, whereas on the other hand, clean videos exhibit piecewise smoothness along the rain-perpendicular direction and continuity along the temporal direction. Theses smoothness and continuity results in the sparse distribution in the different directional gradient domain, respectively. Thus, we minimize 1) the 1 norm to enhance the sparsity of the underlying rain streaks, 2) two 1 norm of unidirectional Total Variation (TV) regularizers to guarantee the anisotropic spatial smoothness, and 3) an 1 norm of the time-directional difference operator to characterize the temporal continuity. A split augmented Lagrangian shrinkage algorithm (SALSA) based algorithm is designed to solve the proposed minimization model. Experiments conducted on synthetic and real data demonstrate the effectiveness and efficiency of the proposed method. According to comprehensive quantitative performance measures, our approach outperforms other state-of-the-art methods, especially on account of the running time. The code of FastDeRain can be downloaded at https://github.com/TaiXiangJiang/FastDeRain. Index Terms-video rain streak removal, unidirectional total variation, split augmented Lagrangian shrinkage algorithm (SALSA) .

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“…The de-raining algorithms investigated in this paper cover a wide variety of techniques, which include the guided filter, dictionary learning, low-rank approximation, maximum posteriori, directional regularization and deep neural network. For short, we denote them by Ding16 [7], Kang12 [10], Luo15 [11], Li16 [12], Deng17 [24], and Fu17 [14]. In our investigation, all codes are provided by authors and the default parameters are used without additional fine-tuning procedure.…”

Section: A Image Collectionmentioning

confidence: 99%

Subjective and Objective De-Raining Quality Assessment Towards Authentic Rain Image

Wang

Ngan

et al. 2020

IEEE Trans. Circuits Syst. Video Technol.

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Images acquired by outdoor vision systems easily suffer poor visibility and annoying interference due to the rainy weather, which brings great challenge for accurately understanding and describing the visual contents. Recent researches have devoted great efforts on the task of rain removal for improving the image visibility. However, there is very few exploration about the quality assessment of de-rained image, even it is crucial for accurately measuring the performance of various de-raining algorithms. In this paper, we first create a deraining quality assessment (DQA) database that collects 206 authentic rain images and their de-rained versions produced by 6 representative single image rain removal algorithms. Then, a subjective study is conducted on our DQA database, which collects the subject-rated scores of all de-rained images. To quantitatively measure the quality of de-rained image with nonuniform artifacts, we propose a bi-directional feature embedding network (B-FEN) which integrates the features of global perception and local difference together. Experiments confirm that the proposed method significantly outperforms many existing universal blind image quality assessment models. To help the research towards perceptually preferred de-raining algorithm, we will publicly release our DQA database and B-FEN source code on https://github.com/wqb-uestc.Index Terms-Single image de-raining, authentic rain image, de-raining quality assessment.

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A directional global sparse model for single image rain removal

Cited by 134 publications

References 23 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

FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors

Subjective and Objective De-Raining Quality Assessment Towards Authentic Rain Image

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