Rain Removal in Video by Combining Temporal and Chromatic Properties

Zhang, Xiao-Peng; Li, Hao; Qi, Yingyi; Leow, Wee Kheng; Ng, T.-Y.

doi:10.1109/icme.2006.262572

Cited by 285 publications

(193 citation statements)

References 8 publications

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“…Gary and Nayar (2004; used intensity changes between consecutive frames to detect raindrops, then compute binary rain field to distinguish rain regions from other moving objects. Zhang (2006) proposed a detection method using chromatic properties of each pixel. The two methods are pixel-based and need multi-frames to get a statistical result.…”

Section: Previous Workmentioning

confidence: 99%

An Improved Guidance Image Based Method to Remove Rain and Snow in a Single Image

Xu¹,

Zhao²,

Liu³

et al. 2012

CIS

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Rain and snow bring poor visibility at outdoor vision systems. The common used image processing methods may be not suitable for a degraded image. In this paper, a guidance image method is proposed to remove rain and snow in a single image. To removal rain and snow only using one image, a guidance image is derived from the imaging model of a raindrop or a snowflake when it is passing through an element on the CCD of the camera. Since only using this guidance image may lose some detailed information, in this paper, a refined guidance image is proposed. This refined guidance image has similar contour with the un-degraded image and also maintains the detailed information which may be lost at the guidance image. Then a removal procedure is given by the use of the refined guidance image. Some comparison results are made between different methods using the guidance image and the refined guidance image. The refined guidance image can be used to get a better removal result. Our results show that this proposed method has both good performance in rain removal and snow removal.

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Section: Previous Workmentioning

confidence: 99%

An Improved Guidance Image Based Method to Remove Rain and Snow in a Single Image

Xu¹,

Zhao²,

Liu³

et al. 2012

CIS

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“…A median filter method was proposed by Hase [11], which makes use of the temporal property of rain steaks. Zhang [12] extended this method by k-means clustering involving chromatic constrains. Brewer and Liu [13] combined the aspect ratio and the orientation of rain streaks into the rain detection, which efficiently reduce false detection.…”

Section: Introductionmentioning

confidence: 99%

“…Obviously, temporal redundant information is applied for rain or snow detection. Such as [8,12,15], neighboring frames are incorporated into the whole detection framework according to the characteristics of rain steaks in temporal field. However, both spatial and temporal methods rely on image/video spatial and temporal redundancy.…”

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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“…Zhang et al performed to avoid distortions in camera and object motion. [13] suggest correction for camera motion prior to rain This paper proposes an image processmig procedure to carry removal. The idea is to extract camera motion, by frame out rain removal in image sequences without explicit motion alignment, before processing and once processed for rain analysis.…”

mentioning

confidence: 99%

“…Since intensity difference is subtracted from corrupted pixels, this does not introduce blufing similar to temporal filtering [6]. Frequency analysis [1], temporal and chromatic analysis [13] …”

mentioning

confidence: 99%

Low latency mitigation of rain induced noise in images

Subhani

Oakley

2008

IET 5th European Conference on Visual Media Production (CVMP 2008)

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The particles (rain drops and snow flakes) are usually large enough to be visible to camera. The particulates affect image Abstract intensity in a complex way and to some extent partially Sometimes, weather conditions, paocclude the necessary image information (see Figure 1).Sometimes, weather conditions, particularly rami and snow, Studies in the field of atmospheric sciences, especially remote makes it difficult to obtain clear image or videos Partial sensing, show that the affect of particles in dynamic weather occlusion by moving rain drops makes it difficult to process are predictable [2,12,9]. such videos for example in object recognition and feature Dynamic weather affects have been removed to some extent tracking. The difficulty further increases in presence of by use of temporal filtering [11]. However, this causes camera and objects motion. Motion analysis is typically bluffing especially when camera or objects move. Zhang et al performed to avoid distortions in camera and object motion. [13] suggest correction for camera motion prior to rain This paper proposes an image processmig procedure to carry removal. The idea is to extract camera motion, by frame out rain removal in image sequences without explicit motion alignment, before processing and once processed for rain analysis. The proposed method analysis temporal changes removal the extracted motion is injected again in the video. (with l frame latency) and selects rain corrupted image Frame alignment may not be possible for example in presence pixels. The selected pixels are further filtered for camera and of object motion. Extraction of motion information in object motion by using spatio-temporal consistency presence of rain is difficult [1] and may produce unreliable constrains. The corrupted pixels are recovered by intensity results. In more recent research, Garg and Nayar [6] suggest subtraction. A naive selection of algorithms shows segmentation of individual rain streaks. They assume comparable results. The proposed scheme has various stationary background within a predefined spatial locality. applications for example outdoor surveillance, feature This allows removal for limited dynamic scenes, however, tracking and object recognition. fails when rain is seen against a moving textured background.

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Rain Removal in Video by Combining Temporal and Chromatic Properties

Cited by 285 publications

References 8 publications

An Improved Guidance Image Based Method to Remove Rain and Snow in a Single Image

An Improved Guidance Image Based Method to Remove Rain and Snow in a Single Image

A Bayesian Probabilistic Framework for Rain Detection

Low latency mitigation of rain induced noise in images

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