• Journal of IKEEE
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• v.22 no.3
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• pp.585-589
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• 2018

Consumer demand for high quality image/video services led to growing trend in image quality enhancement study. Therefore, recent years was a period of substantial progress in this research field. Through careful observation of the image quality after processing by image enhancement algorithms, we perceived that the dark region in the image usually suffered loss of contrast to a certain extent. In this paper, the low-light stretch preprocessing algorithm is, hence, proposed to resolve the aforementioned issue. The proposed approach is evaluated qualitatively and quantitatively against the well-known histogram equalization and Photoshop curve adjustment. The evaluation results validate the efficiency and superiority of the low-light stretch over the benchmarking methods. In addition, we also propose the 255MHz-capable hardware implementation to ease the process of incorporating low-light stretch into real-time imaging systems, such as aerial surveillance and monitoring with drones and driving aiding systems.

• Journal of IKEEE
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• v.18 no.2
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• pp.234-239
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• 2014

This paper proposes a new automatic detection method to inspect mura defects on display film surface using morphological image processing and labeling. This automatic detection method for mura defects on display films comprises 3 phases of preprocessing with morphological image processing, Gabor filtering, and labeling. Since distorted results could be obtained with the presence of non-uniform illumination, preprocessing step reduces illumination components using morphological image processing. In Gabor filtering, mura images are created with binary coded mura components using Gabor filters. Subsequently, labeling is a final phase of finding the mura defect area using the difference between large mura defects and values in the periphery. To evaluate the accuracy of the proposed detection method, detection rate was assessed by applying the method in 200 display film samples. As a result, the detection rate was high at about 95.5%. Moreover, the study was able to acquire reliable results using the Semu index for luminance mura in image quality inspection.

• Journal of IKEEE
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• v.26 no.4
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• pp.639-646
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• 2022

In this paper, to solve the large dataset problem, we collect images through an image collection method called web crawling and build datasets for use in image classification models through a data preprocessing process. We also propose a lightweight model that can automatically classify images by adding category values by incorporating transfer learning into the image classification model and an image classification model that reduces training time and achieves high accuracy.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1738-1743
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• 2017

This paper presents an efficient image stitching method using preprocessing in order to generate a super resolution image. Two-dimensional (2D) scanners are consistently used in various areas but they have limitations such as paper sizes and materials. To overcome these problem with low-cost, an efficient imaging stitching method is proposed for producing a super resolution panorama image. To scan a very large sized paper using mobile phones, a simple portable cradle which fixes height is employed producing an input image set. To improve matching performance, a preprocessing method is introduced before searching correspondences. Then alpha blending is applied to an input image set to produce a super resolution panorama image. The proposed method is faster and easier than the existing method which is employed by Open CV. Experiment results show that the proposed method is three times faster and performs better than the existing method.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.390-393
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• 2006

본 논문에서는 현재 개발 중인 통신해양기상위성(COMS : Communication, Ocean and Meteorological Satellite)의 데이터를 처리하는 영상 데이터 전처리 시스템 (IMPS, IMage Preprocessing Subsystem)의 설계 과정과 예비설계 결과를 설명한다.

• Proceedings of the IEEK Conference
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• 2003.07e
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• pp.1799-1802
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• 2003

This paper has been studied a Adaptive feature-factors based fingerprints recognition in many biometrics. we study preprocessing and matching method of fingerprints image in various circumstances by using optical fingerprint input device. The Fingerprint Recognition Technology had many development until now. But, There is yet many point which the accuracy improves with operation speed in the side. First of all we study fingerprint classification to reduce existing preprocessing step and then extract a Feature-factors with direction information in fingerprint image. Also in the paper, we consider minimization of noise for effective fingerprint recognition system.

• The Journal of the Korea Contents Association
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• v.13 no.10
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• pp.62-70
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• 2013

DIBR(Depth Image Based Rendering) is a kind of view synthesis algorithm to generate images at free view points from the reference color image and its depth map. One of the main challenges of DIBR is the occurrence of holes that correspond to uncovered backgrounds at the synthesized view. In order to cover holes efficiently, two main approaches have been actively investigated. One is to develop preprocessing algorithms for depth maps or parallax maps to reduce the size of possible holes, and the other is to develop hole filling methods to fill the generated holes using adjacent pixels in non-hole areas. Most conventional preprocessing algorithms for reducing the size of holes are based on the smoothing process of depth map. Filtering of depth map, however, attenuates the resolution of depth map and generates geometric distortions. In this paper, we proposes a novel preprocessing algorithm for parallax map to improve the performance of hole-filling by avoiding the drawbacks of conventional methods.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.959-961
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• 1995

The objective of most image proccessing applications is to extract meaningful information from one or more pictures. It is accomplished efficiently using neural networks, which is used in image classification and image recognition. In neural networks, background and meaningful information are processed with same weight in input layer. In this paper, we propose the image classification method using neural networks, especially EBP(Error Back Propagation). Preprocessing is needed. In preprocessing, background is compressed and meaningful information is emphasized. We use the quadtree approach, which is a hierarchical data structure based on a regular decomposition of space.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.2
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• pp.173-178
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• 2014

In this study, we introduce ASM-based face recognition classifier and its design methodology with the aid of 2-dimensional 2-directional hybird preprocessing algorithm. Since the image of face recognition is easily affected by external environments, ASM(active shape model) as image preprocessing algorithm is used to resolve such problem. In particular, ASM is used widely for the purpose of feature extraction for human face. After extracting face image area by using ASM, the dimensionality of the extracted face image data is reduced by using $(2D)^2$hybrid preprocessing algorithm based on LDA and PCA. Face image data through preprocessing algorithm is used as input data for the design of the proposed polynomials based radial basis function neural network. Unlike as the case in existing neural networks, the proposed pattern classifier has the characteristics of a robust neural network and it is also superior from the view point of predictive ability as well as ability to resolve the problem of multi-dimensionality. The essential design parameters (the number of row eigenvectors, column eigenvectors, and clusters, and fuzzification coefficient) of the classifier are optimized by means of ABC(artificial bee colony) algorithm. The performance of the proposed classifier is quantified through yale and AT&T dataset widely used in the face recognition.

• The Journal of the Korea Contents Association
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• v.9 no.11
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• pp.309-315
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• 2009

Image reconstruction of Inverse Fourier Transform after Frequency Domain Data is filtered applies to Image signal acquired from MR. There are various kinds of image processing techniques; image preprocessing, image reconstruction, image compression, image restoration image mixture, noise and artifact elimination, and image quality improvement. In this paper, optimum filter applicable to diagnosis in clinic by comparing and analyzing the characteristics of the filter will be explained. Fermi-Dirac filter will improve the image quality better than the previous MR image.