• Journal of Korea Multimedia Society
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• v.8 no.3
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• pp.313-321
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• 2005

Digital halftoning is a process to produce a binary image so that the original image and its binary counterpart appear similar when observed from a distance. Among digital halftoning methods, error diffusion is a procedure for generating high quality bilevel images from continuous-tone images but blurs the edge information in the bilevel images. To solve this problem, we propose the improved error diffusion using local spatial information of the original images. Based on the fact that the human vision perceives not a pixel but local mean of input image, we compute edge enhancement information(EEI) by appling the ratio of a pixel and its adjacent pixels to local mean. The weights applied to local means is computed using the ratio of local activity measure(LAM) to the difference between input pixels of 3$\times$3 blocks and theirs mean. LAM is the measure of luminance changes in local regions and is obtained by adding the square of the difference between input pixels of 3$\times$3 blocks and theirs mean. We add the value to a input pixel of quantizer to enhance edge. The performance of the proposed method is compared with conventional methods by measuring the edge correlation. The halftone images by using the proposed method show better quality due to the enhanced edge. And the detailed edge is preserved in the halftone images by using the proposed method. Also the proposed method improves the quality of halftone images because unpleasant patterns for human visual system are reduced.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.1105-1118
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• 2014

In this paper, a new reversible data hiding method based on a dual binary tree of embedding levels is proposed. Four neighborhood pixels in the upper, below, left and right of each pixel are used as reference pixels to estimate local complexity for deciding embeddable and non-embeddable pixels. The proposed method does not need to record pixels that might cause underflow, overflow or unsuitable for embedment. This can reduce the size of location map and release more space for payload. Experimental results show that the proposed method is more effective in increasing payload and improving image quality than some recently proposed methods.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.38 no.5
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• pp.556-566
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• 2001

This paper proposes a motion-adaptive de-interlacing algorithm based on EBMF(Edge Based Median Filter) and AMPDF(Adaptive Minimum Pixel Difference Fillet). To compensate 'motion missing'error, which is an important factor in motion-adaptive methods, we used AMPDF which estimates an accurate value using different thresholds after classifying the input image to 4 classes. To efficiently interpolate the moving diagonal edge, we also used EBMF which selects a candidate pixel according to the edge information. Finally, we, to increase the performance, adopted an adaptive interpolation after classifying the input image to moving region, stationary region, and boundary region. Simulation results showed that the proposed method provides better performance than the existing methods.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.514-519
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• 1992

To make an accurate retrieval of the proportion of each category among mixed pixels (Mixel's) of a remotely sensed imagery, a maximum likelihood estimation method of category proportion is proposed. In this method, the observed multispectral vector is considered as probability variables along with the approximation that the supervised data of each category can be characterized by normal distribution. The results show that this method can retrieve accurate proportion of each category among Mixel's. And a index that can estimate the degree of error in each category is proposed. AS one of the application of the proportion estimation, a method for image classification based on category proportion estimation is proposed. In this method all pixel in a remotely sensed imagery are assumed to be Mixel's, and are classified to most dominant category. Among the Mixel's, there exists unconfidential pixels which should be categorized as unclassified pixels. In order to discriminate them, two types of criteria, Chi square and AIC, are proposed for fitness test on pure pixel hypothesis. Experimental result with a simulated dataset show an usefulness of proposed classification criterion compared to the conventional maximum likelihood criterion and applicability of the fitness tests based on Chi square and AIC,

• Journal of Sensor Science and Technology
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• v.21 no.3
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• pp.211-216
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• 2012

Fires are the most common disaster and early fire detection is of great importance to minimize the consequent damage. Simple sensors including smoke detectors are widely used for the purpose but they are able to sense fires only at close proximity. Recently, due to the rapid advances of relevant technologies, vision-based fire sensing has attracted growing attention. In this paper, a novel visual sensing technique to automatically detect fire is presented. The proposed technique consists of multiple steps of image processing: pixel-level, block-level, and frame level. At the first step, fire flame pixel candidates are selected based on their color values in YIQ space from the image of a camera which is installed as a vision sensor at a fire scene. At the second step, the dynamic parts of flames are extracted by comparing two consecutive images. These parts are then represented in regularly divided image blocks to reduce pixel-level detection error and simplify following processing. Finally, the temporal change of the detected blocks is analyzed to confirm the spread of fire. The proposed technique was tested using real fire images and it worked quite reliably.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.52 no.11
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• pp.626-633
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• 2003

Theoretical analysis of the depth measurement system with the use of a single camera and a rotating mirror has been done. A camera in front of a rotating mirror acquires a sequence of reflected images, from which depth information is extracted. For an object point at a longer distance, the corresponding pixel in the sequence of images moves at a higher speed. Depth measurement based on such pixel movement is investigated. Since the mirror rotates along an axis that is in parallel with the vertical axis of the image plane, the image of an object will only move horizontally. This eases the task of finding corresponding image points. In this paper, the principle of the depth measurement-based on the relation of the pixel movement speed and the depth of objects have been investigated. Also, necessary mathematics to implement the technique is derived and presented. The factors affecting the measurement precision have been studied. Analysis shows that the measurement error increases with the increase of depth. The rotational angle of the mirror between two image-takings also affects the measurement precision. Experimental results using the real camera-mirror setup are reported.

• Journal of the Korea Society of Computer and Information
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• v.23 no.4
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• pp.147-153
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• 2018

In this paper, we propose an efficient WBC 14-Diff classification which performs using the WBC-ResNet-152, a type of CNN model. The main point of view is to use Super-pixel for the segmentation of the image of WBC, and to use ResNet for the classification of WBC. A total of 136,164 blood image samples (224x224) were grouped for image segmentation, training, training verification, and final test performance analysis. Image segmentation using super-pixels have different number of images for each classes, so weighted average was applied and therefore image segmentation error was low at 7.23%. Using the training data-set for training 50 times, and using soft-max classifier, TPR average of 80.3% for the training set of 8,827 images was achieved. Based on this, using verification data-set of 21,437 images, 14-Diff classification TPR average of normal WBCs were at 93.4% and TPR average of abnormal WBCs were at 83.3%. The result and methodology of this research demonstrates the usefulness of artificial intelligence technology in the blood cell image classification field. WBC-ResNet-152 based morphology approach is shown to be meaningful and worthwhile method. And based on stored medical data, in-depth diagnosis and early detection of curable diseases is expected to improve the quality of treatment.

• Journal of the Optical Society of Korea
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• v.17 no.6
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• pp.513-517
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• 2013

Recently, maskless lithography (ML) systems have become popular in digital manufacturing technologies. To achieve high-throughput manufacturing processes, digital micromirror devices (DMD) in ML systems must be driven to their operational limits, often in harsh conditions. We propose an instrument and algorithm to detect DMD malfunctions to ensure perfect mask image transfer to the photoresist in ML systems. DMD malfunctions are caused by either bad DMD pixels or data transfer errors. We detect bad DMD pixels with $20{\times}20$ pixel by white and black image tests. To analyze data transfer errors at high frame rates, we monitor changes in the frame rate of a target DMD pixel driven by the input data with a set frame rate of up to 28000 frames per second (fps). For our data transfer error detection method, we verified that there are no data transfer errors in the test by confirming the agreement between the input frame rate and the output frame rate within the measurement accuracy of 1 fps.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.81.3-81.3
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• 2019

In this study, we apply conditional Generative Adversarial Network, which is one of the deep learning method, to the image-to-image translation from solar magentograms to solar UV and EUV images. For this, we train a model using pairs of SDO/AIA 9 wavelength UV and EUV images and their corresponding SDO/HMI line-of-sight magnetograms from 2011 to 2017 except August and September each year. We evaluate the model by comparing pairs of SDO/AIA images and corresponding generated ones in August and September. Our results from this study are as follows. First, we successfully generate SDO/AIA like solar UV and EUV images from SDO/HMI magnetograms. Second, our model has pixel-to-pixel correlation coefficients (CC) higher than 0.8 except 171. Third, our model slightly underestimates the pixel values in the view of Relative Error (RE), but the values are quite small. Fourth, considering CC and RE together, 1600 and 1700 photospheric UV line images, which have quite similar structures to the corresponding magnetogram, have the best results compared to other lines. This methodology can be applicable to many scientific fields that use several different filter images.

• Korean Journal of Remote Sensing
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• v.38 no.1
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• pp.33-43
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• 2022

This study presents a method to restore an optical satellite image with distortion and occlusion due to fog, haze, and clouds to one that minimizes degradation factors by referring to the same type of peripheral image. Specifically, the time and cost of re-photographing were reduced by partially occluding a region. To maintain the original image's pixel value as much as possible and to maintain restored and unrestored area continuity, a simulation restoration technique modified with the Cycle Generative Adversarial Network (CycleGAN) method was developed. The accuracy of the simulated image was analyzed by comparing CycleGAN and histogram matching, as well as the pixel value distribution, with the original image. The results show that for Site 1 (out of three sites), the root mean square error and R² of CycleGAN were 169.36 and 0.9917, respectively, showing lower errors than those for histogram matching (170.43 and 0.9896, respectively). Further, comparison of the mean and standard deviation values of images simulated by CycleGAN and histogram matching with the ground truth pixel values confirmed the CycleGAN methodology as being closer to the ground truth value. Even for the histogram distribution of the simulated images, CycleGAN was closer to the ground truth than histogram matching.