• Journal of the Semiconductor & Display Technology
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• v.20 no.1
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• pp.108-112
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• 2021

Undesired reflection removal from an image captured through glass window is widely needed with the prevalence of camera. In this paper, we present and implement a reflection removal algorithm, which is specially designed for smart devices. Our implementation requires smart phone application to take two input pictures of the same target, one with flash light on and another with flash light off. Then, we find a flash spot in the picture, match the features to align the input pictures, transform the color space, and finally combine the pictures. As the result, we get a resulting image with removed reflection, achieving the visually pleasant.

• Journal of the Semiconductor & Display Technology
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• v.18 no.4
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• pp.154-159
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• 2019

Undesirable negative image is occurred in photographs taken across partial reflections such as glass window and electronic display. Efficient removing reflections given a single image are in the spotlight in recent researches. This paper discusses and evaluates two published image reflection removal algorithms, and compares the performance of time and quality of those methods with a common dataset. As benchmarking test cases are presented, we propose to modify one of the methods to reduce the run-time with small effects on the similar image quality.

• Proceedings of the Korea Multimedia Society Conference
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• 2012.05a
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• pp.26-27
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• 2012

Reflection considered as the view disturbing noise in optical systems, such as stereo camera in autonomous vehicles especially in night. Reflection caused by the street light or due to rainwater under adverse weather conditions. A blur image detected by the camera that results in wrong guidance to vehicle for detecting its track. A vehicle guidance approach through stereo vision can be same in day and night time. However it cannot be guided with same image analysis due to diverse illumination conditions. We develop the technique that shows its efficacy with illustrations of reflection removal off the camera lens and vehicle tracking control.

• The Journal of Korea Robotics Society
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• v.18 no.3
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• pp.271-280
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• 2023

FMCW (Frequency Modulated Continuous Wave) radar system is widely used in autonomous driving and navigation applications due to its high detection capabilities independent of weather conditions and environments. However, radar signals can be easily contaminated by various noises such as speckle noise, receiver saturation, and multipath reflection, which can worsen sensing performance. To handle this problem, we propose a learning-free noise removal technique for radar to enhance detection performance. The proposed method leverages adaptive thresholding to remove speckle noise and receiver saturation, and wavelet transform to detect multipath reflection. After noise removal, the radar image is reconstructed with the geometric structure of the surrounding environments. We verify that our method effectively eliminated noise and can be applied to autonomous driving by improving the accuracy of odometry and place recognition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.6
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• pp.1136-1142
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• 2010

In this study, an efficient removal algorithm for specular reflections in a tooth color image is proposed to minimize the artefact interrupting color image segmentation. The pixel values of RGB color channels are initially reversed to emphasize the features in reflective regions, and then those regions are automatically detected by utilizing perceptron artificial neural network model and those prominent intensities are corrected by applying a smoothing spatial filter. After correcting specular reflection regions, multiple seeds in the tooth candidates are selected to find the regional minima and MCWA(Marker-Controlled Watershed Algorithm) is applied to delineate the individual tooth region in a CCD tooth color image. Therefore, the accuracy in segmentation for separating tooth regions can be drastically improved with removing specular reflections due to the illumination effect.

• Proceedings of the Korean Information Science Society Conference
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• 2004.10b
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• pp.688-690
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• 2004

In this paper, we propose a new method of removing glasses from human frontal facial images. We first detect the regions occluded by the glasses, and generate a natural looking facial image without glasses by recursive error compensation using PCA reconstruction. The resulting image has no trace of the glasses frame, nor of the reflection and shade caused by the glasses. The experimental results show that the proposed method provides an effective solution to the problem of glasses occlusion, and we believe that this method can also be used to enhance the performance of face recognition systems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.13 no.1
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• pp.67-72
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• 2013

Cloud detection and analysis from satellite images has been a topic of research in many atmospheric and environmental studies; however, it still is a challenging task for many reasons. In this paper, we propose a new method for cloud-type classification using fuzzy logic. Knowing that visible-light images of clouds contain thickness related information, while infrared images haves height-related information, we propose a two-layered fuzzy logic based on the input source to provide us with a relatively clear-cut threshold in classification. Traditional noise-removal methods that use reflection/release characteristics of infrared images often produce false positive cloud areas, such as fog thereby it negatively affecting the classification accuracy. In this study, we used the color information from source images to extract the region of interest while avoiding false positives. The structure of fuzzy inference was also changed, because we utilized three types of source images: visible-light, infrared, and near-infrared images. When a cloud appears in both the visible-light image and the infrared image, the fuzzy membership function has a different form. Therefore we designed two sets of fuzzy inference rules and related classification rules. In our experiment, the proposed method was verified to be efficient and more accurate than the previous fuzzy logic attempt that used infrared image features.

• Advances in environmental research
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• v.3 no.2
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• pp.163-172
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• 2014

Magnetite was chosen as a typical adsorbent to study its phosphate adsorption capacity in water body with low concentration of phosphorus (below $2mg\;PL^{-1}$). Magnetite was collected from Luoyang City, Henan Province, China. In this research, three factors have been studied to describe the adsorption of phosphate on magnetite, which was solution concentration (concentration ranging from 0.1 to $2.5mg\;PL^{-1}$), suspension pH (1 to 13) and temperature (ranging from $10^{\circ}C$ to $40^{\circ}C$). In addition, the modified samples had been characterized with XRD and FE-SEM image. The results show that iron ions contains in magnetite were the main factors of phosphorus removal. The behavior of phosphorus adsorption to substrates could be fitted to both Langmuir and Freundlich isothermal adsorption equations in the low concentration phosphorus water. The theoretical saturated adsorption quantity of magnetite is 0.158 mg/g. pH has great influence on the phosphorus removal of magnetite ore by adsorption. And pH of 3 can receive the best results. While temperature has little effect on it. Magnetite was greatly effective for phosphorus removal in the column experiments, which is a more practical reflection of phosphorous removal combing the adsorption isotherm model and the breakthrough curves. According to the analysis of heavy metals release, the release of heavy metals was very low, they didn't produce the secondary pollution. The mechanism of uptake phosphate is in virtue of chemisorption between phosphate and ferric ion released by magnetite oxidation. The combined investigation of the magnetite showed that it was better substrate for water body with low concentration of phosphorus.

• Korean Journal of Agricultural Science
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• v.47 no.4
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• pp.995-1010
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• 2020

In this study, a multivariate analysis model of partial least square regression (PLSR) was developed to predict the moisture content of green peppers using hyperspectral imaging (HSI). In HSI, illumination is essential for high-quality image acquisition and directly affects the analytical performance of the visible near-infrared hyperspectral imaging (VIS/NIR-HSI) system. When green pepper images were acquired using a direct lighting system, the specular reflection from the surface of the objects and their intensities fluctuated with time. The images include artifacts on the surface of the materials, thereby increasing the variability of data and affecting the obtained accuracy by generating false-positive results. Therefore, images without glare on the surface of the green peppers were created using a polarization filter at the front of the camera lens and by exposing the polarizer sheet at the front of the lighting systems simultaneously. The results obtained from the PLSR analysis yielded a high determination coefficient of 0.89 value. The regression coefficients yielded by the best PLSR model were further developed for moisture content mapping in green peppers based on the selected wavelengths. Accordingly, the polarization filter helped achieve an uniform illumination and the removal of gloss and artifact glare from the green pepper images. These results demonstrate that the HSI technique with a polarized lighting system combined with chemometrics can be effectively used for high-throughput prediction of moisture content and image-based visualization.

• Geophysics and Geophysical Exploration
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• v.23 no.3
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• pp.168-177
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• 2020

Seabed multiple reflections (seabed multiples) are the main cause of misinterpretations of primary reflections in both shot gathers and stack sections. Accordingly, seabed multiples need to be suppressed throughout data processing. Conventional model-driven methods, such as prediction-error deconvolution, Radon filtering, and data-driven methods, such as the surface-related multiple elimination technique, have been used to attenuate multiple reflections. However, the vast majority of processing workflows require time-consuming steps when testing and selecting the processing parameters in addition to computational power and skilled data-processing techniques. To attenuate seabed multiples in seismic reflection data, input gathers with seabed multiples and label gathers without seabed multiples were generated via numerical modeling using the Marmousi2 velocity structure. The training data consisted of normal-moveout-corrected common midpoint gathers fed into a U-Net neural network. The well-trained model was found to effectively attenuate the seabed multiples according to the image similarity between the prediction result and the target data, and demonstrated good applicability to field data.