• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.8
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• pp.891-897
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• 1992

A local iterative Image restoration method Is Introduced that processes with varying iteration numbers according to the local statistics. In general almost of the Iterative method applies Its algorithm to the whole Image without considering the local pixel informations, which Is not so effective for the processing time. Usually the edges or details have an Important role In visual effect. So in this paper we process the edges or the details many times while In the flat region we just pass over or decrease iterations. This method shows good MSE (Mean Square Error) improvement, better subjective qualify and reduced processing time.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1237-1239
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• 1996

3-D object recognition and restoration independent of the translation and rotation using an ultrasonic sensor array, neural networks and invariant moment are presented. Using invariant moment vectors on the acquired $16{\times}8$ pixel data, 3-D objects can be classified by SOFM(Self Organizing Feature Map) neural networks. Invariant moment vectors kept constant independent of the translation and rotation. The experiment result shows the suggested method can be applied to the environment recognition.

• Journal of the Semiconductor & Display Technology
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• v.23 no.2
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• pp.23-27
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• 2024

Underwater images are typically degraded due to color distortion, light absorption, scattering, and noise from artificial light sources. Restoration of these images is an essential task in many underwater applications. In this paper, we propose a two-phase deep learning-based method, Underwater Deep Curve Estimation (UWDCE), designed to effectively enhance the quality of underwater images. The first phase involves a white balancing and color correction technique to compensate for color imbalances. The second phase introduces a novel deep learning model, UWDCE, to learn the mapping between the color-corrected image and its best-fitting curve parameter maps. The model operates iteratively, applying light-enhancement curves to achieve better contrast and maintain pixel values within a normalized range. The results demonstrate the effectiveness of our method, producing higher-quality images compared to state-of-the-art methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.21 no.10
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• pp.2541-2551
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• 1996

Human visual systems are sensitive to noise on the flat intensity area. But it becomes less sensitive on the edge area. Recently, many types of spatially adaptive image restoration methods have been proposed, which employ the above mentioned huan visual characteristics. The present paper presents an adaptive image restoration method, which increases sharpness of the edge region, and smooths noise on the flat intensity area. For edge detection, the proposed method uses the visibility function based on the local variance on each pixel. And it adaptively changes the regularization parameter. More specifically, the image to be restored is divided into a number of steps from the flat area to the edge regio, and then restored by using the finite impulse response constrained least squares filter.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.12
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• pp.17-23
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• 1997

A 3-D vision system is developed to evaluate blanks' line up and monitor gap and thickness difference between blanks in tailored blank welding system. A structured lighting method is used for 3-D vision recognition. Images of sheared portion in blanks are irregular according to roughness of blank surface, shape of sheared geometry and blurring. It is difficult to get accurate and reliable informations in the case of using binary image processing or contour detection techniques in real time for such images. We propoe a new energy integration method robust to blurring and changes of illumination. The method is computationally simple, and uses feature restoration concept, different to another digital image restoration methods which aim image itself restoration and may be used in conventional applications using structured line lighting technique. Experimental results show this system measuring repeatability is .+-. pixel for gap and thickness difference in static and dynamic tests. The data are expected to be useful for preview gap control.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1747-1761
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• 2023

Wetlands, defined as lands periodically inundated or exposed during the year, are crucial for sustaining biodiversity and filtering environmental pollutants. The importance of mapping and monitoring their topographical changes is therefore paramount. This study focuses on the topographical variations at the Incheon River estuary wetland post-restoration, noting a lack of adequate prior measurements. Using a multi-temporal Sentinel-1 dataset from October 2014 to March 2023, we mapped long-term variations in water bodies and detected topographical change anomalies using a pixel-frequency approach. Our analysis, based on 196 Sentinel-1 acquisitions from an ascending orbit, revealed significant topography changes. Since 2020, employing the pixel-frequency technique, we observed area increases of +0.0195, 0.0016, 0.0075, and 0.0163 km² in water level sections at depths of 2-3 m, 1-2 m, 0-1 m, and less than 0 m, respectively. These findings underscore the effectiveness of the wetland restoration efforts in the area.

• Korean Journal of Remote Sensing
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• v.36 no.5_4
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• pp.1231-1244
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• 2020

Landsat satellites are representative optical satellites that have observed the Earth's surface for a long-term, and are suitable for long-term changes such as disaster preparedness/recovery monitoring, land use change, change detection, and time series monitoring. In this paper, clouds and cloud shadows were detected using QA bands to detect and remove clouds simply and efficiently. Then, the missing area of the experimantal image is restorated through the SSG algorithm, which does not directly refer to the pixel value of the reference image, but performs restoration to the pixel value in the Experimental image. Through this study, we presented the possibility of utilizing the modified SSG algorithm by quantitatively and qualitatively evaluating information on variousl and cover conditions in the thermal wavelength band as well as the visible wavelength band observing the surface.

• KIPS Transactions on Software and Data Engineering
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• v.3 no.1
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• pp.43-48
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• 2014

Usually, image deconvolution is applied as a preprocessing step in surveillance systems to reduce the effect of motion or out-of-focus blur problem. In this paper, we propose a blind-image deconvolution filtering approach based on genetic programming (GP). A numerical expression is developed using GP process for image restoration which optimally combines and exploits dependencies among features of the blurred image. In order to develop such function, first, a set of feature vectors is formed by considering a small neighborhood around each pixel. At second stage, the estimator is trained and developed through GP process that automatically selects and combines the useful feature information under a fitness criterion. The developed function is then applied to estimate the image pixel intensity of the degraded image. The performance of developed function is estimated using various degraded image sequences. Our comparative analysis highlights the effectiveness of the proposed filter.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.6
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• pp.85-96
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• 2023

This study proposes a method for forest vegetation monitoring using high-resolution aerial imagery captured by unmanned aerial vehicles(UAV) and deep learning technology. The research site was selected in the forested area of Mountain Dogo, Asan City, Chungcheongnam-do, and the target species for monitoring included Pinus densiflora, Quercus mongolica, and Quercus acutissima. To classify vegetation species at the pixel level in UAV imagery based on characteristics such as leaf shape, size, and color, the study employed the semantic segmentation method using the prominent U-net deep learning model. The research results indicated that it was possible to visually distinguish Pinus densiflora Siebold & Zucc, Quercus mongolica Fisch. ex Ledeb, and Quercus acutissima Carruth in 135 aerial images captured by UAV. Out of these, 104 images were used as training data for the deep learning model, while 31 images were used for inference. The optimization of the deep learning model resulted in an overall average pixel accuracy of 92.60, with mIoU at 0.80 and FIoU at 0.82, demonstrating the successful construction of a reliable deep learning model. This study is significant as a pilot case for the application of UAV and deep learning to monitor and manage representative species among climate-vulnerable vegetation, including Pinus densiflora, Quercus mongolica, and Quercus acutissima. It is expected that in the future, UAV and deep learning models can be applied to a variety of vegetation species to better address forest management.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.6
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• pp.36-42
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• 2007

According to the increased demand of high quality multimedia content, it needs to recover an old movies. But the film of old movie is damaged with line scratches and dust. In this paper, the detection and restoration algorithm of B/W line scratch is proposed. Our scheme estimates and interpolates the damaged partial information of line scratch using the linear recursive curve trace which consider the intensity values of left and right region of line scratch and then median filtering processed. As a result, the film image PSNR 44.68 with B/W line scratch is increased up to 48.60 and the intensity of the interpolate pixel is approached about 14 against the pixel of original image.