• 사물인터넷융복합논문지
• /
• 제10권2호
• /
• pp.51-60
• /
• 2024

저조도 환경에서 영상 이미지의 콘트라스트가 낮고 식별이 어려운 문제를 목표로 사람의 시각 감지 기반의 콘트라스트 적응 보상 증진 알고리즘을 제안한다. 첫째, 저조도 환경에서 평균 밝기, 평균 대역폭 요인의 영상 이미지 특징 요인을 추출하고, 원본 영상의 회색/색도 차이에 따라 사람의 시각적 콘트라스트 해상도 보상의 수학적 모델을 설정하며, 실제 컬러의 3원색에 대해 각각 비례 적분하여 보상한다. 다음으로 보상 정도가 명시각 차이를 적절하게 구별할 수 있는 것보다 낮을 때 보상 임계값 선형 보상이 명시각에서 전체 대역폭으로 설정된다. 마지막으로 주관적인 이미지 품질 평가와 이미지 특성 요인을 결합하여 비례 계수를 보상하는 자동 최적화 모델을 구축한다. 실험 테스트 결과는 영상 이미지 적응 증진 알고리즘이 우수한 증진 효과와 우수한 실시간 성능을 가지며 다크 비전 정보를 효과적으로 마이닝할 수 있으며 다양한 시나리오에서 널리 사용될 수 있음을 보여준다.

• 자동차안전학회지
• /
• 제11권4호
• /
• pp.16-21
• /
• 2019

In recent years, most automobile manufacturers have been using slim light source images to imprint their own brand image and identity. A light guide type lamp is widely used for making these kinds of a slim light source image. A light guide lamp means using a light emitting diode light source at one end of a long cylindrical pipe. The light from the light emitting diode source moves through the pipe by total reflection principle. Moving light is sent forward by various optic structures which is applied in the cylindrical pipe. However, the light guide lamp has a problem that the image of the light differs depending on the viewing direction, and in some cases there is dark section. It means light guide has low quality about lighting image. In this paper, trying to improve the fundamental problems of the light guide mentioned above by using various triz methods. Through functional modeling, estimating the factors affecting the light in the light guide lamp and make various ideas to improve the lighting image using the chain effect cause analysis, function oriented search scientific database techniques. Using these kinds of various TRIZ methods, finally find solutions that can improve the brightness and lighting uniformity of the light guide lamp. The ideas obtained in this paper were applied to actual vehicle development, and several patents achievements were obtained. In conclusion, it is proved that TRIZ method is useful for making ideas in actual automobile industrial field and is also a useful method for acquiring patent.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제18권4호
• /
• pp.980-997
• /
• 2024

To improve the brightness of images and reveal hidden information in dark areas is the main objective of low-light image enhancement (LLIE). LLIE methods based on deep learning show good performance. However, there are some limitations to these methods, such as the complex network model requires highly configurable environments, and deficient enhancement of edge details leads to blurring of the target content. Single-scale feature extraction results in the insufficient recovery of the hidden content of the enhanced images. This paper proposed an edge detection-based multi-scale feature enhancement network for LLIE (EDMFEN). To reduce the loss of edge details in the enhanced images, an edge extraction module consisting of a Sobel operator is introduced to obtain edge information by computing gradients of images. In addition, a multi-scale feature enhancement module (MSFEM) consisting of multi-scale feature extraction block (MSFEB) and a spatial attention mechanism is proposed to thoroughly recover the hidden content of the enhanced images and obtain richer features. Since the fused features may contain some useless information, the MSFEB is introduced so as to obtain the image features with different perceptual fields. To use the multi-scale features more effectively, a spatial attention mechanism module is used to retain the key features and improve the model performance after fusing multi-scale features. Experimental results on two datasets and five baseline datasets show that EDMFEN has good performance when compared with the stateof-the-art LLIE methods.

• 마이크로전자및패키징학회지
• /
• 제27권2호
• /
• pp.1-10
• /
• 2020

The technology and market size of image sensors continue to develop thanks to the release of image sensors that exceed 100 million pixels in 2019 and expansion of black box camera markets for vehicles in addition to existing mobile applications. We review the technology flow of image sensors that have been constantly evolving for 40 years since Hitachi launched a 200,000-pixel image sensor in 1979. Although CCD has made inroads into image sensor market for a while based on good picture quality, CMOS image sensor (CIS) with active pixels has made inroads into the market as semiconductor technology continues to develop, since the electrons generated by the incident light are converted to the electric signals in the pixel, and the power consumption is low. CIS image sensors with superior characteristics such as high resolution, high sensitivity, low power consumption, low noise and vivid color continue to be released as the new technologies are incorporated. At present, new types of structures such as Backside Illumination and Isolation Cell have been adopted, with better sensitivity and high S/N ratio. In the future, new photoconductive materials are expected to be adopted as a light absorption part in place of the pn junction.

• Journal of the Optical Society of Korea
• /
• 제15권4호
• /
• pp.357-361
• /
• 2011

Photon-counting sensing is a widely used technique for low-light-level imaging applications. This paper proposes a distance information extraction method with photon-counting passive sensing under low-lightlevel conditions. The photo-counting passive sensing combined with integral imaging generates a photon-limited elemental image array. Maximum-likelihood estimation (MLE) is used to reconstruct the photon-limited image at certain depth levels. The distance information is extracted at the depth level that minimizes the sum of the standard deviation of the corresponding photo-events in the elemental image array. Experimental and simulation results confirm that the proposed method can extract the distance information of the object under low-light-level conditions.

• 전기전자학회논문지
• /
• 제23권4호
• /
• pp.1465-1468
• /
• 2019

The image noise improvement algorithm proposed in this paper extracts the boundary line by using the window of the binarized image to detect the gesture motion. Boundary line blurring is prevented by improving Gaussian noise generated during video output. To improve gesture recognition in low-light environments, an image noise enhancement algorithm has been designed to provide an output image close to the base image. Analyzing the experimental results, we found almost 10% improvement in the results compared to the results of the existing Median filter.

• International Journal of Internet, Broadcasting and Communication
• /
• 제7권1호
• /
• pp.15-19
• /
• 2015

The depth information of an image is used in a variety of applications including 2D/3D conversion, multi-view extraction, modeling, depth keying, etc. There are various methods to acquire depth information, such as the method to use a stereo camera, the method to use the depth camera of flight time (TOF) method, the method to use 3D modeling software, the method to use 3D scanner and the method to use a structured light just like Microsoft's Kinect. In particular, the depth camera of TOF method measures the distance using infrared light, whereas TOF sensor depends on the sensitivity of optical light of an image sensor (CCD/CMOS). Thus, it is mandatory for the existing image sensors to get an infrared light image by bundling several pixels; these requirements generate a phenomenon to reduce the resolution of an image. This thesis proposed a measure to acquire a high-resolution image through gradual area movement while acquiring a low-resolution image through pixel bundling method. From this measure, one can obtain an effect of acquiring image information in which illumination intensity (lux) and resolution were improved without increasing the performance of an image sensor since the image resolution is not improved as resolving a low-illumination intensity (lux) in accordance with the gradual pixel bundling algorithm.

• 방송공학회논문지
• /
• 제24권4호
• /
• pp.623-632
• /
• 2019

저조도 영상의 개선을 위해서 밝기 및 대조 개선, 조명 성분 감쇄 등의 다양한 연구가 진행됐다. 기존의 hand-crafted 방법에서 인공신경망으로 기존 기법들을 대체하는 연구가 최근에 진행 중이다. 본 논문에서는 조명 광원이 존재하는 저조도 영상으로부터 조명 성분을 감쇄하고, 반사 성분만을 생성하는 기법을 합성곱 신경망으로 대체하는 방법을 제안한다. 실험에서는 102장의 저조도 영상으로 학습시킨 합성곱 신경망으로 만족스러운 반사 영상을 생성하였다.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제55권4호
• /
• pp.161-164
• /
• 2006

In this thesis we designed the vision system to inspect the defect of a back light unit of plat panel display device. The vision system is divided into hardware and inspection algorithm of defect. Hardware components consist of illumination part, robot-arm controller part and image-acquisition part. Illumination part is made of acrylic panel for light diffusion and five 36W FPL's(Fluorescent Parallel Lamp) and electronic ballast with low frequency harmonics. The CCD(Charge-Coupled Device) camera of image-acquisition part is able to acquire the bright image by the light coming from lamp. The image-acquisition part is composed of CCD camera and frame grabber. The robot-arm controller part has a role to let the CCD camera move to the desired position. To take inspections of surface images of a flat panel display it can be controlled and located every nook and comer. Images obtained by robot-arm and image-acquisition board are saved on the hard-disk through windows programming and are tested whether there are defects by using the image processing algorithms.

• 한국컴퓨터정보학회논문지
• /
• 제21권12호
• /
• pp.11-18
• /
• 2016

Magnetic resonance image (MRI) is widely used in brain research field and medical image. Especially, non-invasive brain activation acquired image technique, which is functional magnetic resonance image (fMRI) is used in brain study. In this study, we investigate brain activation occurred by LED light stimulation. For investigate of brain activation in experimental small animal, we used high magnetic field 9.4T MRI. Experimental small animal is Balb/c mouse, method of fMRI is using echo planar image (EPI). EPI method spend more less time than any other MRI method. For this reason, however, EPI data has low contrast. Due to the low contrast, image pre-processing is very hard and inaccuracy. In this study, we planned the study protocol, which is called block design in fMRI research field. The block designed has 8 LED light stimulation session and 8 rest session. All block is consist of 6 EPI images and acquired 1 slice of EPI image is 16 second. During the light session, we occurred LED light stimulation for 1 minutes 36 seconds. During the rest session, we do not occurred light stimulation and remain the light off state for 1 minutes 36 seconds. This session repeat the all over the EPI scan time, so the total spend time of EPI scan has almost 26 minutes. After acquired EPI data, we performed the analysis of this image data. In this study, we analysis of EPI data using statistical parametric map (SPM) software and performed image pre-processing such as realignment, co-registration, normalization, smoothing of EPI data. The pre-processing of fMRI data have to segmented using this software. However this method has 3 different method which is Gaussian nonparametric, warped modulate, and tissue probability map. In this study we performed the this 3 different method and compared how they can change the result of fMRI analysis results. The result of this study show that LED light stimulation was activate superior colliculus region in mouse brain. And the most higher activated value of segmentation method was using tissue probability map. this study may help to improve brain activation study using EPI and SPM analysis.