• Journal of Broadcast Engineering
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• v.20 no.1
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• pp.153-163
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• 2015

This paper proposes a rain detection and removal algorithm that is robust against camera motion in video sequences. In detection part, the proposed algorithm initially detects possible rain streaks by using intensity properties and spatial properties. Then, the rain streak candidates are selected based on Gaussian distribution model. In removal part, a non-rain block matching algorithm is performed between adjacent frames to find similar blocks to the block that has rain pixels. If the similar blocks to the block are obtained, the rain region of the block is reconstructed by non-local means (NLM) filter using the similar neighbors. Experimental results show that the proposed algorithm outperforms the previous works in terms of subjective visual quality of de-rained video sequences.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.9
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• pp.96-105
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• 2015

This paper proposes a DSP optimization solution of rain detection and removal algorithm. We propose rain detection and removal algorithms considering camera motion, and also presents optimization results in algorithm level and DSP level. At algorithm level, this paper utilizes a block level binary pattern analysis, and reduces the operation time by using the fast motion estimation algorithm. Also, the algorithm is optimized at DSP level through inter memory optimization, EDMA, and software pipelining for real-time operation. Experiment results show that the proposed algorithm is superior to the other algorithms in terms of visual quality as well as processing speed.

• Journal of Korea Multimedia Society
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• v.22 no.1
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• pp.1-9
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• 2019

Single image rain removal is a typical inverse problem which decomposes the image into a background scene and a rain streak. Recent works have witnessed a substantial progress on the task due to the development of convolutional neural network (CNN). However, existing CNN-based approaches train the network with synthetically generated training examples. These data tend to make the network bias to the synthetic scenes. In this paper, we present an unsupervised framework for removing rain streaks from real-world rainy images. We focus on the natural phenomena that static rainy scenes capture a common background but different rain streak. From this observation, we train siamese network with the real rain image pairs, which outputs identical backgrounds from the pairs. To train our network, a real rainy dataset is constructed via web-crawling. We show that our unsupervised framework outperforms the recent CNN-based approaches, which are trained by supervised manner. Experimental results demonstrate that the effectiveness of our framework on both synthetic and real-world datasets, showing improved performance over previous approaches.

• IEMEK Journal of Embedded Systems and Applications
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• v.9 no.6
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• pp.345-351
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• 2014

This paper presents pedestrian recognition to improve performance for vehicle safety system or surveillance system. Pedestrian detection method using HOG (Histograms of Oriented Gradients) has showed 90% recognition rate. But if someone takes a picture in the rain, the image may be distorted by rain streaks and recognition rate goes down by 62%. To solve this problem, we applied image decomposition method using MCA (Morphological Component Analysis). In this case, rain removal method improves recognition rate from 62% to 70%. However, it is difficult to apply conventional image decomposition method using MCA on vehicle safety system or surveillance system as conventional method is too slow for real-time system. To alleviate this issue, we propose a rain removal method by using low-pass filter and DCT (Discrete Cosine Transform). The DCT helps separate the image into rain components. The image is removed rain components by Butterworth filtering. Experimental results show that our method achieved 90% of recognition rate. In addition, the proposed method had accelerated processing time to 17.8ms which is acceptable for real-time system.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.81-88
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• 2017

This paper proposes a method of detecting rain regions from a single image. More specifically, a way of training the deep convolutional neural network based on the collected rain and non-rain patches is presented in a supervised manner. It is also shown that the proposed rain detection method based on deep convolutional neural network can provide better performance than the conventional rain detection method based on dictionary learning. Moreover, it is confirmed that the application of the proposed rain detection for rain removal can lead to some improvement in detail representation on the low-frequency regions of the rain-removed images. Additionally, this paper introduces the rain transfer method that inserts rain patterns into original images, thereby producing rain effects on the resulting images. The proposed rain transfer method could be used to augment rain patterns while constructing rain database.

• Journal of Korean Society for Atmospheric Environment
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• v.21 no.E3
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• pp.87-94
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• 2005

Wet scavenging by rain drops is a most important removal process of air pollutants. In order to study the scavenging mechanisms of aerosol particles, the characteristics of chemical components in the rain water were examined as a function of the amount of rainfall. Rain water were collected continuously and separated into the soluble and insoluble components. The elemental concentrations in both components were determined by a PIXE analysis. The physical and chemical characteristics of atmospheric aerosols during the rainfall events were measured simultaneously. The elemental concentrations in rain water decreased substantially just after rain started and then gradually declined in subsequential rain fall exceeding 1.0 mm. The large particles were scavenged more easily than the fine particles. Fe, Ti and Si in rain water were in high insoluble state. Contrarily, almost whole of S was dissolved in rain water.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.11a
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• pp.113-117
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• 2020

Images were taken under various weather such as rain, haze, snow often show low visibility, which can dramatically decrease accuracy of some tasks in computer vision: object detection, segmentation. Besides, previous work to enhance image usually downsample the image to receive consistency features but have not yet good upsample algorithm to recover original size. So, in this research, we jointly implement removal streak in heavy rain image and super resolution using a deep network. We put forth a 2-stage network: a multi-model network followed by a refinement network. The first stage using rain formula in the single image and two operation layers (addition, multiplication) removes rain streak and noise to get clean image in low resolution. The second stage uses refinement network to recover damaged background information as well as upsample, and receive high resolution image. Our method improves visual quality image, gains accuracy in human action recognition task in datasets. Extensive experiments show that our network outperforms the state of the art (SoTA) methods.

• Journal of Korean Society on Water Environment
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• v.28 no.5
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• pp.676-685
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• 2012

As impermeable layer continues to increase with the urbanization process, direct input of nonpoint source pollutants into water bodies via stormwater has caused serious effects on the aquatic ecosystem. Potential applications of rain gardens are increasing not only as best management practices (BMP) for reducing the level of nonpoint source pollutants but also as an ecological engineering alternative for low impact development (LID). In this study, remediation performance of various planting types, such as a mixed planting system with shrubs and herbaceous plants, was assessed quantitatively to effectively manage stormwater and increase landscape applicability. The mixed planting system with Rhododendron lateritium and Zoysia japonica showed the highest removal performance of $76.9{\pm}7.6%$ and $58.4{\pm}5.0%$ for total nitrogen and $89.9{\pm}7.9%$ and $82.4{\pm}5.2%$ for total phosphorus at rainfall intensities of 2.5 mm/h and 5.0 mm/h, respectively. The mixed planting system also showed the highest removal performance for heavy metals. The results suggest that a rain garden with the mixed planting system has high potential applicability as a natural reduction system for nonpoint source pollutants in order to manage stormwater with low concentrations of pollutants and will increase water recycling in urban areas.

• Journal of Wetlands Research
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• v.17 no.1
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• pp.38-44
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• 2015

In Korea, most rainfall events occur during summer which then leads to an increasing concern regarding high influx of non-point source pollutants since the pollutant loadings from these non-point sources are very significant. In particular, the first flush of roof-harvested rainfall is said to contain the most highest concentration of nutrients and heavy metals. Accordingly, it is important to develope the possible water quality management options in treating the contaminants and considering reclaimed water reuse. The rain garden could be one of suitable alternatives in addressing this issue. In this study, the development of an effective adsorption media and its application to a lab-scale rain garden was tested to evaluate the removal rate of various nutrient and organic matter (TN, TP, CODcr), and heavy metals (Cu, Cd, Pb). Results showed that carbonized peatmoss produced at higher temperature have better adsorption capacity as compared to the one produced at a lower temperature. When the carbonized peatmoss was applied as rain garden media, the highest removal of TN, TP, and CODcr was observed compared to no carbonized peatmoss applied rain garden. Therefore, this study showed that the carbonized peatmoss would be effectively applied to the rain garden for removing nutrients and heavy metals from roof-harvested rainwater.

• Proceedings of the Korea Air Pollution Research Association Conference
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• 2006.10a
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• pp.47-50
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• 2006