• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.9
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• pp.1281-1285
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• 2014

Recently, as the wide spread of smart phone, pico projector which is used at the smart phone is appeared as a portable display device. In this paper, among several pico projectors, laser beam scanner module is dealt with in which laser is used as light source, and mems-mirror is used as optical panel. In this device, screen image quality is a special issue, and blur effect is a typical adverse effect to the quality of this device. So the enhancement of this blur effect has an important factor of the quality of the device. The definition of the blur and the main source of the blur are studied and the simulation results and way of improvement are also suggested.

• International Journal of Computer Science & Network Security
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• v.22 no.1
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• pp.1-6
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• 2022

Exploration has always been an instinct of humans, and underwater life is as fascinating as it seems. So, for studying flora and fauna below water, there is a need for high-quality images. However, the underwater images tend to be of impaired quality due to various factors, which calls for improved and enhanced underwater images. There are various Histogram Equalization (HE) based techniques which could aid in solving these issues. Classifying the HE methods broadly, there is Global Histogram Equalization (GHE), Mean Brightness Preserving HE (MBPHE), Bin Modified HE (BMHE), and Local HE (LHE). Each of these HE extensions have their own pros and cons and thus, by considering them we have considered BBHE, CLAHE, BPDHE, BPDFHE, and DSIHE enhancement algorithms, which are based on Mean Brightness Preserving HE and Local HE, for this study. The performance is evaluated with non-reference performance measures like Entropy, UCIQE, UICM, and UIQM. In this study, we apply the enhancement algorithms on 300 images from the UIEB benchmark dataset and then apply the techniques of cascading fusion on the best-performing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.7
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• pp.2390-2406
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• 2022

Scene text recognition has important application value and attracted the interest of plenty of researchers. At present, many methods have achieved good results, but most of the existing approaches attempt to improve the performance of scene text recognition from the image level. They have a good effect on reading regular scene texts. However, there are still many obstacles to recognizing text on low-quality images such as curved, occlusion, and blur. This exacerbates the difficulty of feature extraction because the image quality is uneven. In addition, the results of model testing are highly dependent on training data, so there is still room for improvement in scene text recognition methods. In this work, we present a natural scene text recognizer to improve the recognition performance from the feature level, which contains feature representation and feature enhancement. In terms of feature representation, we propose an efficient feature extractor combined with Representative Batch Normalization and ResNet. It reduces the dependence of the model on training data and improves the feature representation ability of different instances. In terms of feature enhancement, we use a feature enhancement network to expand the receptive field of feature maps, so that feature maps contain rich feature information. Enhanced feature representation capability helps to improve the recognition performance of the model. We conducted experiments on 7 benchmarks, which shows that this method is highly competitive in recognizing both regular and irregular texts. The method achieved top1 recognition accuracy on four benchmarks of IC03, IC13, IC15, and SVTP.

• Journal of Broadcast Engineering
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• v.16 no.4
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• pp.584-595
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• 2011

Recently, RGB images and depth maps have been supplied to academic fields. The depth maps are utilized to the generation of stereoscopic images in the diverse formats according to the users' preference. A variety of methods that use depth maps have been introduced so far. One of applications is a medical field. In this area, the improvement of the perceptual quality of 2D medical images has gained much interest. In this paper, we propose a novel scheme that expands the conventional method to 3D stereoscopic image, thereby achieving the perceptual depth quality improvement as well as 3D stereoscopic perception enhancement at the same time. For this, contrast transformation as well as depth darkening are proposed and their performance is validated through the subjective test. Subjective experiments peformed for stereoscopic enhancement as well as visual fatigue validate that the proposed method achieves better 3D perception than the usage of the original stereoscopic image and suggests the limitation in terms of the visual fatigue.

• 대한방사선방어학회:학술대회논문집
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• 2009.11a
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• pp.118-119
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• 2009

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.5
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• pp.9-16
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• 2018

Images with lower illumination from the light source or with dark regions due to shadows, etc., can improve subjective image quality by using retinex-based image enhancement schemes. The retinex theory is a method that recognizes the relative lightness of a scene, rather than recognizing the brightness of the scene. The way the human visual system recognizes a scene in a specific position can be in one of several methods: single-scale retinex, multi-scale retinex, and multi-scale retinex with color restoration (MSRCR). The proposed method is based on the MSRCR method, which includes a color restoration step, which consists of three phases. In the first phase, the existing MSRCR method is applied. In the second phase, the dynamic range of the MSRCR output is adjusted according to its histogram. In the last phase, the proposed method transforms the retinex output value into the display dynamic range using a logarithm transformation function considering human visual system characteristics. Experimental results show that the proposed algorithm effectively increases the subjective image quality, not only in dark images but also in images including both bright and dark areas. Especially in a low lightness image, the proposed algorithm showed higher performance improvement than the conventional approaches.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.787-795
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• 2009

This paper presents a histogram equalization based on the logistic function for enhancing the quality of images. The histogram equalization is a simple and effective spatial processing method that it enhances the quality by adjusting the brightness of image. The logistic function that is a nonlinear transformation function is applied to adaptively enhance the brightness of the image according to its intensity level frequency. We propose a flexible and asymmetrical logistic function by only using the intensity level with maximum frequency and the maximum intensity level in an histogram, and the total number of pixels. The proposed function excludes both the computation load of an exponential function and the heuristic setting of an optimal parameter values in the traditional logistic function. The proposed method has been applied for equalizing many images with a different resolution and histogram distribution. The experimental results show that the proposed method has the superior enhancement performances and the faster equalizing speed compared with the traditional histogram equalization and the adaptively modified histogram equalization, respectively. And the proposed histogram equalization can be used in various multimedia systems in real-time.

• 한국정보디스플레이학회:학술대회논문집
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• 2004.08a
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• pp.239-242
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• 2004

This paper presents a novel contrast enhance algorithm for images displayed with bright environment light. This algorithm is designed to preserve local contrast based on the luminance ratio of the pixel to its local surround in attention. This algorithm improves image quality of projectors in a bright room.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.11a
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• pp.213-216
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• 2002

The system realization for printimage enhancement of electrothermal printer using thermal historic management is presented. The thermal historic control technique reduces the blur that the high density thermal print head(TPH) and high speed printing in the 300dpi high is increased edge blur of printed image. The experiment result is that the system enhance the quality of print image.

• The Journal of Korea Robotics Society
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• v.19 no.2
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• pp.188-195
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• 2024

This paper proposes a photorealistic real-time dense 3D mapping system that utilizes a neural network-based image enhancement method and mesh-based map representation. Due to the characteristics of the underwater environment, where problems such as hazing and low contrast occur, it is hard to apply conventional simultaneous localization and mapping (SLAM) methods. At the same time, the behavior of Autonomous Underwater Vehicle (AUV) is computationally constrained. In this paper, we utilize a neural network-based image enhancement method to improve pose estimation and mapping quality and apply a sliding window-based mesh expansion method to enable lightweight, fast, and photorealistic mapping. To validate our results, we utilize real-world and indoor synthetic datasets. We performed qualitative validation with the real-world dataset and quantitative validation by modeling images from the indoor synthetic dataset as underwater scenes.