Rain Detection Research Articles

Multi-frame de-raining algorithm using a motion-compensated non-local mean filter for rainy video sequences

This paper proposes a rain detection and removal algorithm that is robust against camera motion. The proposed algorithm initially detects possible rain streaks by using spatial properties, such as the luminance and structure of rain streaks. Then, the rain streak candidates are selected based on a Gaussian distribution model. Finally, these detected regions are improved with an advanced temporal property in a block-matching process. After the rain detection step, a non-rain block-matching algorithm for each block is performed between adjacent frames to find blocks similar to the block that has rain pixels. If similar blocks are obtained, the rain region of the block is reconstructed by spatio-temporal non-local mean filtering using similar neighboring regions. Finally, a specific post-processing is performed for visibility enhancement and flickering artifact removal. Experiment results show that the proposed algorithm uses only five temporally adjacent frames for rain removal but outperforms previous methods in terms of subjective visual quality.

Rain Identification in ASCAT Winds Using Singularity Analysis

The Advanced Scatterometer (ASCAT) onboard the Metop satellite series is designed to measure the global ocean surface wind vector. Generally, ASCAT provides wind products at excellent quality. Occasionally, though, ASCAT-derived winds are degraded by rain. Therefore, identification of rain can help to better understand the rain impact on scatterometer wind quality and to develop a proper quality control (QC) approach for scatterometer data processing. In this letter, an image processing method, known as singularity analysis (SA), is used to detect the presence of rain such that rain-contaminated wind vector cells are flagged. The performance of SA for rain detection is validated using ASCAT Level-2 data collocated with satellite radiometer rain data. The rain probability as a function of SA singularity exponent is calculated and compared with other rain sensitive parameters, such as the wind inversion residual or maximum-likelihood estimator (MLE). The results indicate that the SA is effective in detecting ASCAT rain-contaminated data. Moreover, SA is a complementary rain indicator to the MLE parameter, thus showing great potential for an improved scatterometer QC.

A Bayesian Probabilistic Framework for Rain Detection

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.

Removal of rain in video based on motion and shape characteristics of raindrops

Rain degrades the performance of the outdoor computer vision system, which adversely impacts tracking, object detection and recognition. To solve this problem, a novel rain detection and removal method is proposed in this paper. Firstly, raindrops are considered as the motion component and extracted by FastICA algorithm. Secondly, raindrops are identified based on its properties of area, direction and width. Finally, a two-step removal method is proposed to replace pixels covered by rain with estimated background pixels. Experimental results show that the algorithm can effectively remove the raindrops in various videos. Compared with traditional rain removal methods, it does not require any parameters about rain, camera and scenes, and only needs three successive frames to recover a raindrops free video.

A Method for Detection and Removal of Rain in Videos

摘要: 降雨天气往往导致监控视频质量下降. 本文提出首先在对数图像处理(Logarithmic image processing, LIP)框架下利用灰色调约束检测出候选雨滴,进而利用主成分分析(Principal component analysis, PCA)方法计算每个候选雨滴的倾斜方向并构建其概率密度分布函数,利用Mean-shift算法估计该分布函数的峰值,作为检测到的雨滴降落方向,然后,通过方向约束去除候选雨滴中的干扰噪声. 最后,文章采用一种加权的重构方法消除雨滴. 实验证明,算法能够有效检测并去除各种场景中的雨滴. 关键词: 对数图像处理 / 均值漂移 / 主成分分析 / 雨滴消除

Using NWP Simulations in Satellite Rainfall Estimation of Heavy Precipitation Events over Mountainous Areas

Abstract In this study, the authors investigate the use of high-resolution simulations from the Weather Research and Forecasting Model (WRF) for evaluating satellite rainfall biases of flood-inducing storms in mountainous areas. A probability matching approach is applied to evaluate a power-law relationship between satellite-retrieved and WRF-simulated rain rates over the storm domain. Satellite rainfall in this study is from the NOAA Climate Prediction Center morphing technique (CMORPH). Results are presented based on analyses of five heavy precipitation events that induced flash floods in northern Italy and southern France complex terrain basins. The WRF-based adjusted CMORPH rain rates exhibited improved error statistics against independent radar rainfall estimates. The authors show that the adjustment procedure reduces the underestimation of high rain rates, thus moderating the magnitude dependence of CMORPH rainfall bias. The Heidke skill score for the WRF-based adjusted CMORPH was consistently higher for a range of rain rate thresholds. This is an indication that the adjustment procedure ameliorates the satellite rain rates to provide a better estimation. Results also indicate that the low rain detection of CMORPH technique is also identifiable in the WRF–CMORPH comparison; however, the adjustment procedure herein does not incorporate this effect on the satellite rainfall bias adjustment.

Validation of satellite OPEMW precipitation product with ground-based weather radar and rain gauge networks

Abstract. The Precipitation Estimation at Microwave Frequencies (PEMW) algorithm was developed at the Institute of Methodologies for Environmental Analysis of the National Research Council of Italy (IMAA-CNR) for inferring surface rain intensity (sri) from satellite passive microwave observations in the range from 89 to 190 GHz. The operational version of PEMW (OPEMW) has been running continuously at IMAA-CNR for two years. The OPEMW sri estimates, together with other precipitation products, are used as input to an operational hydrological model for flood alert forecast. This paper presents the validation of OPEMW against simultaneous ground-based observations from a network of 20 weather radar systems and a network of more than 3000 rain gauges distributed over the Italian Peninsula and main islands. The validation effort uses a data set covering one year (July 2011–June 2012). The effort evaluates dichotomous and continuous scores for the assessment of rain detection and quantitative estimate, respectively, investigating both spatial and temporal features. The analysis demonstrates 98% accuracy in correctly identifying rainy and non-rainy areas; it also quantifies the increased ability (with respect to random chance) to detect rainy and non-rainy areas (0.42–0.45 Heidke skill score) or rainy areas only (0.27–0.29 equitable threat score). Performances are better than average during summer, fall, and spring, while worse than average in the winter season. The spatial–temporal analysis does not show seasonal dependence except over the Alps and northern Apennines during winter. A binned analysis in the 0–15 mm h−1 range suggests that OPEMW tends to slightly overestimate sri values below 6–7 mm h−1 and underestimate sri above those values. With respect to rain gauges (weather radars), the correlation coefficient is larger than 0.8 (0.9). The monthly mean difference and standard deviation remain within ±1 and 2 mm h−1 with respect to rain gauges (respectively −2–0 and 4 mm h−1 with respect to weather radars).

A Rain Pixel Recovery Algorithm for Videos With Highly Dynamic Scenes

Rain removal is a very useful and important technique in applications such as security surveillance and movie editing. Several rain removal algorithms have been proposed these years, where photometric, chromatic, and probabilistic properties of the rain have been exploited to detect and remove the rainy effect. Current methods generally work well with light rain and relatively static scenes, when dealing with heavier rainfall in dynamic scenes, these methods give very poor visual results. The proposed algorithm is based on motion segmentation of dynamic scene. After applying photometric and chromatic constraints for rain detection, rain removal filters are applied on pixels such that their dynamic property as well as motion occlusion clue are considered; both spatial and temporal informations are then adaptively exploited during rain pixel recovery. Results show that the proposed algorithm has a much better performance for rainy scenes with large motion than existing algorithms.

Validation Study on Jason-2 Rain Flag and Rain Rate Estimation Over North Indian Ocean

This letter reports the results of the validation study on Jason-2 (JA2) rain flag and rain rate (RR) estimation over North Indian Ocean for the first time. This study was performed using 12 months of Geophysical Data Record (GDR) and collocated Microwave Imager and Precipitation Radar (PR) observations onboard Tropical Rainfall Measuring Mission (TRMM) employing strict spatial and temporal collocation criteria. The present study brought out that rain flag parameter available in GDR was not triggered during rain events, and an attempt is made to recompute the same using a novel method of differential attenuation between Ku band and C band backscatter. The score of rain detection using recomputed Ku/C mean rain-free relation is found to be about 98%. The high rain detection by JA2 clearly demonstrates the performance of the dual-frequency rain flagging while considering the high temporal and spatial variability of the rain. The comparison of RR estimated from JA2 with TRMM sensors was in good agreement. The results of the present study thus accentuate the need for better rain flagging over regional oceans which is essential to evade erroneous values in altimetric geophysical parameters.

Footstep and Vehicle Detection Using Slow and Quick Adaptive Thresholds Algorithm

An algorithm is developed for footstep, vehicle, and rain detection using seismic sensors operating in a wireless sensor network. Each standalone seismic sensor is coupled with a wireless node, and alarm conditions were evaluated at the sensor rather than at the gateway. The algorithm utilizes slow and quick adaptive thresholds to eliminate static and dynamic noise to check for any disturbance. Duration calculation and filters were used to identify the correct alarm condition. The algorithm was performed on preliminary field tests, and detection performance was verified. Footstep alarm condition up to 8 meters and vehicle presence alarm condition up to 50 meters were observed. Presence of rain did not create any alarm condition. Detection based on kurtosis was also performed and shortcomings of kurtosis especially for vehicle detection were discussed, proposed algorithm has minimal load on the sensor board and its data processing unit; thus, it is energy efficient and suitable for wireless sensor alarm networks.

The Research Status of Acid Rain

This paper not only makes a detailed introduction about the research of acid rain including the mechanism of formation, the factor of formation, the harm of acid rain and the analysis, detection and forecast of acid rain, but also points out the problem in the research and the control measure of acid rain.

Investigation of the fractal dimension of rainfall occurrence in a semi-arid Mediterranean climate

The scale invariance of rainfall series in the Tunis area, Tunisia (semi-arid Mediterranean climate) is studied in a mono-fractal framework by applying the box counting method to four series of observations, each about 2.5 years in length, based on a time resolution of 5 min. In addition, a single series of daily rainfall records for the period 1873–2009 was analysed. Three self-similar structures were identified: micro-scale (5 min to 2 d) with fractal dimension 0.44, meso-scale (2 d to one week) and synoptic-scale (one week to eight months) with fractal dimension 0.9. Interpretation of these findings suggests that only the micro-scale and transition to saturation are consistent, while the high fractal dimension relating to the synoptic scale might be affected by the tendency to saturation. A sensitivity analysis of the estimated fractal dimension was performed using daily rainfall data by varying the series length, as well as the intensity threshold for the detection of rain. Editor Z.W. Kundzewicz; Associate editor S. Grimaldi Citation Ghanmi, H., Bargaoui, Z., and Mallet, C., 2013. Investigation of the fractal dimension of rainfall occurrence in a semi-arid Mediterranean climate. Hydrological Sciences Journal, 58 (3), 483–497.

Meteorological approach for detection and removal of rain from videos

Rain removal algorithms require certain number of consecutive frames for reducing the visibility of rain from videos. These algorithms are designed for the rain videos captured by the fixed camera. In this study, it is demonstrated that using global motion compensation all the rain removal algorithms developed for fixed camera can be used for moving camera too. In this study a novel, efficient and simple algorithm for detection and removal of rain from video using meteorological properties is proposed. Here meteorological properties are used to separate the rain pixels from the non‐rain pixels. The proposed algorithm achieves good accuracy in spite of less number of consecutive frames, which reduces the buffer size and delay. It works only on the intensity plane that further reduces the complexity and execution time significantly. Quantitative and qualitative analyses show that the proposed algorithm removes rain effectively under constrained buffer size and delay in comparison with most of the competing rain removal algorithms.

Joint Feature Based Rain Detection and Removal from Videos

Joint Feature Based Rain Detection and Removal from Videos

Removal of rain from videos: a review

In this paper, the algorithms for the detection and removal of rain from videos have been reviewed. Rain reduces the visibility of scene and thus performance of computer vision algorithms which use feature information. Detection and removal of rain requires the discrimination of rain and nonrain pixels. Accuracy of the algorithm depends upon this discrimination. Here merits and demerits of the algorithms are discussed, which motivate the further research. A rain removal algorithm has a wide application in tracking and navigation, consumer electronics and entertainment industries.

Using Markov switching models to infer dry and rainy periods from telecommunication microwave link signals

Abstract. A Markov switching algorithm is introduced to classify attenuation measurements from telecommunication microwave links into dry and rainy periods. It is based on a simple state-space model and has the advantage of not relying on empirically estimated threshold parameters. The algorithm is applied to data collected using a new and original experimental set-up in the vicinity of Zürich, Switzerland. The false dry and false rain detection rates of the algorithm are evaluated and compared to 3 other algorithms from the literature. The results show that, on average, the Markov switching model outperforms the other algorithms. It is also shown that the classification performance can be further improved if redundant information from multiple channels is used.

Evaluation of the climate prediction center (CPC) morphing technique (CMORPH) rainfall product on hourly time scales over the source of the Blue Nile River

AbstractLimited availability of surface‐based rainfall observations constrains the evaluation of satellite rainfall products over many regions. Observations are also often not available at time scales to allow evaluation of satellite products at their finest resolutions. In the present study, we utilized a 3‐month rainfall data set from an experimental network of eight automatic gauges in Gilgel Abbay watershed in Ethiopia to evaluate the 1‐hourly, 8 × 8‐km Climate Prediction Center morphing technique (CMORPH) rainfall product. The watershed is situated in the Lake Tana basin which is the source of the Blue Nile River. We applied a suite of statistical metrics that included mean difference, bias, standard deviation of differences and measures of association. Our results indicate that the accuracy of the CMORPH product shows a significant variation across the basin area. Its estimates are mostly within ±10 mm h−1 of the gauge rainfall observations; however, the product does not satisfactorily capture the rainfall temporal variability and is poorly correlated (<0.27) to gauge observations. Its poor rain detection capability led to significant underestimation of the seasonal rainfall depth (total bias reaches up to −52%) with large amounts of hit rain bias as well as missed rain and false rain biases. In the future refinement of CMORPH algorithm, more attention should be given to reducing missed rain bias over the mountains of Gilgel Abbay, whereas equal attention should be given to hit, missed rain and false rain biases over other parts of the watershed. Copyright © 2012 John Wiley & Sons, Ltd.

Challenges to Satellite Sensors of Ocean Winds: Addressing Precipitation Effects

AbstractMeasurements of global ocean surface winds made by orbiting satellite radars have provided valuable information to the oceanographic and meteorological communities since the launch of the Seasat in 1978, by the National Aeronautics and Space Administration (NASA). When Quick Scatterometer (QuikSCAT) was launched in 1999, it ushered in a new era of dual-polarized, pencil-beam, higher-resolution scatterometers for measuring the global ocean surface winds from space. A constant limitation on the full utilization of scatterometer-derived winds is the presence of isolated rain events, which affect about 7% of the observations. The vector wind sensors, the Ku-band scatterometers [NASA’s SeaWinds on the QuikSCAT and Midori-II platforms and Indian Space Research Organisation’s (ISRO’s) Ocean Satellite (Oceansat)-2], and the current C-band scatterometer [Advanced Wind Scatterometer (ASCAT), on the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)’s Meteorological Operation (MetOp) platform] all experience rain interference, but with different characteristics. Over this past decade, broad-based research studies have sought to better understand the physics of the rain interference problem, to search for methods to bypass the problem (using rain detection, flagging, and avoidance of affected areas), and to develop techniques to improve the quality of the derived wind vectors that are adversely affected by rain. This paper reviews the state of the art in rain flagging and rain correction and describes many of these approaches, methodologies, and summarizes the results.

Video post processing: low-latency spatiotemporal approach for detection and removal of rain

In this study, a novel, efficient and simple algorithm for detection and removal of rain from video using spatiotemporal properties is proposed. Advantageously, the spatiotemporal properties are involved to separate rain pixels from non-rain pixels. It is thus possible by way of the proposed algorithm to involve less number of consecutive frames, reducing the buffer size and delay. It works only on the intensity plane which further reduces the complexity and execution time significantly. This new algorithm does not assume the shape, size and velocity of raindrops which makes it robust to different rain conditions. Proposed method reduces the buffer size, which reduces the system cost, delay and power consumption. For performance evaluation, in addition to miss & false detection a new metric spatiotemporal variance is introduced. Results show that the proposed algorithm outperforms the other rain removal algorithms.

A new rain detection method to complement high-resolution global precipitation products

A new rain detection method to complement high-resolution global precipitation products