Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition

Kang, Li‐Wei; Lin, Chia‐Wen; Fu, Ya–Yuan

doi:10.1109/tip.2011.2179057

Cited by 675 publications

(81 citation statements)

References 37 publications

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“…This is caused by the inaccurate decomposition of the high-frequency portion into rain components and non-rain components, which failed to recover the non-rain components and faulty incorporation of the rain components into the low-frequency partition. Similar methods have also been proposed in [4,5], Kang et al [4] proposed a method that employing bilateral filter to divide the image with rain into low-frequency portions and highfrequency portions firstly. The rain component is then extracted from the high-frequency portion by using a sparse representation-based dictionary partition in which the dictionary is classified using HOG in each atom where the bilateral filter is used to separate the low-frequency part from its high-frequency part of an input image.…”

Section: Introductionmentioning

confidence: 96%

Weighted median guided filtering method for single image rain removal

Shi

Zhang³

et al. 2018

J Image Video Proc.

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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.

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Section: Introductionmentioning

confidence: 96%

Weighted median guided filtering method for single image rain removal

Shi

Zhang³

et al. 2018

J Image Video Proc.

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“…In fixed pattern noise, the structure of the noise is often fixed or similar, like the raindrops in a natural image. Fixed pattern noise has been well studied in the literature [7,8,9,10,11]. By comparison, the structure of the noise in the ICCD image is random.…”

Section: Introductionmentioning

confidence: 99%

“…noise removal, there have been some methods for the clustered noise removal, such as water droplets or raindrops in the image. One dominant solution is called self-learning-based signal decomposition [9,10,11]. In this solution, the image is first decomposed into a low-frequency part and a high-frequency part, and a dictionary from the high-frequency part is learned.…”

Section: Introductionmentioning

confidence: 99%

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

Wang

Yang

et al. 2017

Sensors

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An Intensified Charge-Coupled Device (ICCD) image is captured by the ICCD image sensor in extremely low-light conditions. Its noise has two distinctive characteristics. (a) Different from the independent identically distributed (i.i.d.) noise in natural image, the noise in the ICCD sensing image is spatially clustered, which induces unexpected structure information; (b) The pattern of the clustered noise is formed randomly. In this paper, we propose a denoising scheme to remove the randomly clustered noise in the ICCD sensing image. First, we decompose the image into non-overlapped patches and classify them into flat patches and structure patches according to if real structure information is included. Then, two denoising algorithms are designed for them, respectively. For each flat patch, we simulate multiple similar patches for it in pseudo-time domain and remove its noise by averaging all the simulated patches, considering that the structure information induced by the noise varies randomly over time. For each structure patch, we design a structure-preserved sparse coding algorithm to reconstruct the real structure information. It reconstructs each patch by describing it as a weighted summation of its neighboring patches and incorporating the weights into the sparse representation of the current patch. Based on all the reconstructed patches, we generate a reconstructed image. After that, we repeat the whole process by changing relevant parameters, considering that blocking artifacts exist in a single reconstructed image. Finally, we obtain the reconstructed image by merging all the generated images into one. Experiments are conducted on an ICCD sensing image dataset, which verifies its subjective performance in removing the randomly clustered noise and preserving the real structure information in the ICCD sensing image.

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“…Barnum et al [14,15] proposed a global appearance model to formulate rain in the frequency domain. Moreover, a image-based processing method was proposed by Kang [16], which implements rain removal by an image decomposition way based on morphological component analysis (MCA) [17,18]. In Kang's method, image noise removal method (i.e., bilateral filter, K-SVD dictionary train algorithm, etc.)…”

Section: Introductionmentioning

confidence: 99%

“…Spatial techniques consist of one category. These techniques make full use of image spatial correlation, such as [16]. Rain steaks in image/video are regarded as high frequency information.…”

Section: Introductionmentioning

confidence: 99%

A Bayesian Probabilistic Framework for Rain Detection

Yao

Wang

Hong

et al. 2014

Entropy

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Heavy rain deteriorates the video quality of outdoor imaging equipments. In order to improve video clearness, image-based and sensor-based methods are adopted for rain detection. In earlier literature, image-based detection methods fall into spatio-based and temporal-based categories. In this paper, we propose a new image-based method by exploring spatio-temporal united constraints in a Bayesian framework. In our framework, rain temporal motion is assumed to be Pathological Motion (PM), which is more suitable to time-varying character of rain steaks. Temporal displaced frame discontinuity and spatial Gaussian mixture model are utilized in the whole framework. Iterated expectation maximization solving method is taken for Gaussian parameters estimation. Pixels state estimation is finished by an iterated optimization method in Bayesian probability formulation. The experimental results highlight the advantage of our method in rain detection.

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Automatic Single-Image-Based Rain Streaks Removal via Image Decomposition

Cited by 675 publications

References 37 publications

Weighted median guided filtering method for single image rain removal

Weighted median guided filtering method for single image rain removal

A Denoising Scheme for Randomly Clustered Noise Removal in ICCD Sensing Image

A Bayesian Probabilistic Framework for Rain Detection

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