• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.9
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• pp.128-137
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• 2012

When a CCD camera acquires images in the low light level environment, not only the image signals but also noise components are amplified by the AGC (auto gain control) circuit. Since the noise level in the images acquired in the dark is very high, it is difficult to remove noise with existing denoising algorithms that are targeting the images taken in the normal light condition. In this paper, we proposed an adaptive denoising algorithm that can efficiently remove significant noises caused by the low light level. First, the window including a target pixel is transformed to the frequency domain. Then the algorithm compares the characteristics of equally divided four frequency bands. Finally the noises are adaptively removed according to the frequency characteristics. The proposed algorithm successfully improves the quality of low light level images than the existing algorithms do.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.11-19
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• 2023

In this paper, it is confirmed that the metrics used to evaluate image quality can be applied to low-light images. Due to the nature of low-illumination images, factors related to light create various noise patterns, and the smaller the amount of light, the more severe the noise. Therefore, in situations where it is difficult to obtain a clean image without noise, the quality of a low-illuminance image from which noise has been removed is often judged by the human eye. In this paper, noise in low-illuminance images for which ground truth cannot be obtained is removed using Noise2Noise, and spatial resolution and radial resolution are evaluated using ISO 12233 charts and colorchecker as metrics such as MTF and SNR. It can be shown that the quality of the low-illuminance image, which has been evaluated mainly for qualitative evaluation, can also be evaluated quantitatively.

• Journal of the Korean Society for Railway
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• v.13 no.3
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• pp.283-289
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• 2010

Recent advance of digital camera results in that image signal processing techniques are widely adopted to railroad security management. However, due to the nature of railroad management many images are acquired in low light level environment such as night scenes. The lack of light causes lots of noise in the image, which degrades image quality and causes errors in the next processes. 3D noise reducing techniques produce better results by using consecutive sequence of images. On the other hand, they cause degradation such as motion blur if there are motions in the sequence. In this paper, we use an adaptive weight filter to estimate more accurate motions and use the result of the adaptive filter to 3D result to improve objective and subjective mage quality.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2015.11a
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• pp.159-160
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• 2015

본 논문에서는 잡음이 있는 저조도 동영상의 고속 시인성 개선 기법을 제안한다. 먼저, 영상에서 고속 추출한 광도를 기반으로 입력 영상을 저조도 영역과 고조도 영역으로 구분한 뒤, 각 영역의 특징을 반영한 전달 함수의 독립적인 생성 및 적용을 통해 영상의 밝기를 개선한다. 다음으로 동영상의 풍부한 시공간적 정보 활용 극대화를통해 효율적으로 영상의 잡음을 제거한다. 마지막으로 영상의 색상 분포 분석을 통해 매핑 함수를 생성하고, 이를 적용하여 색상 치우침 문제가 있는 저조도 영상의 색상을 효과적으로 복원한다. 실험을 통하여 제안 기법이 기존 기법 대비 우수한 시인성 개선 및 속도 개선 결과를 보임을 확인한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.572-575
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• 2010

본 논문은 SNR 이 매우 낮은 저조도 영상의 잡음 제거를 위한 새로운 기술을 제안한다. 제안하는 기술은 입력 영상에서 파라미터를 자동/적응적 방식으로 추정하는 알고리즘을 특징으로 한다. 제안하는 기술의 효율성을 검증하기 위해 실질적인 환경에서 취득한 저조도 동영상들을 가지고 실험을 수행하였다. 실험을 통해 제안하는 기술을 활용하여 적응적으로 추정된 파라미터가 필터링(filtering) 성능을 잘 유지시킴을 검증하였다. 또한 기존 연구들과 비교할 때 저조도 동영상의 명암대비 향상과 잡음 제거에 우수한 결과를 보임을 검증하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.4
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• pp.90-96
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• 2010

The focusing system is an important factor to determine the imaging quality of a digital imaging system. The focusing system consist of measuring the focusing index with high frequency energy of an image and controlling the movement of the focusing lens based on the computed focusing index. The computation of the focusing index is a key aspect in implementing the focusing system and the noise of the image cause the error in the sharpness evaluation of the image. To reduce this error, the noise under the low illumination condition is considered. A noise insensitive focusing index using adaptive weights is proposed in this paper. This measure determines the sharpness of an image using the spatially adaptive weights based on the local statistics of the image and noise. Experimental results under the condition without and with the noise verify the performance of the proposed method.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.88-98
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• 2021

Recently, the crime prevention and culprit identification even in a low illuminated environment by CCTV is becoming ever more important. In a low lighting situation, CCTV applications capture images under infrared lighting since it is unobtrusive to human eye. Although the infrared lighting leads to advantage of capturing an image with abundant fine texture information, it is hard to capture the color information which is very essential in identifying certain objects or persons in CCTV images. In this paper, we propose a method to acquire color information through DCGAN from an image captured by CCTV in a low lighting environment with infrared lighting and a method to remove color noise in the acquired color image.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2017.06a
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• pp.163-165
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• 2017

본 논문에서는 카메라 잡음 제거에 딥 러닝 알고리즘을 적용하는 연구를 진행하였다. 합성된 가우시언 잡음에 대하여 좋은 잡음 제거 성능을 보이는 DnCNN(Denoising Convolutional Network)를 이용하여 카메라 잡음을 제거하는 학습과 실험을 진행하였으며, 기준 실험으로는 RGB 색공간의 3채널 모두에 대하여 학습한 신경망(Neural Network)을 사용하였고, 본 논문의 실험에서는 그레이 이미지에 대하여 학습한 신경망을 사용하였다. 신경망의 평가를 위하여 딥 러닝 알고리즘 입력 이미지를 RGB 색공간(RGB Color Space)과 YCbCr 색공간(YCbCr Color Space) 2가지 색공간으로 표현하여 사용하였고, 입력 이미지에 노이즈를 첨가하기 위해 가우시안 노이즈(Gaussian Noise)를 이용하였다. 또한 가우시안 잡음과 다른 성질을 갖는 실제 카메라 잡음에 대해서도 학습과 테스트를 진행하였다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2020.07a
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• pp.241-243
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• 2020

저조도 환경에서 획득한 CCTV 컬러 영상은 품질이 좋지 않으므로, 일정 조도 이하의 저조도에서 CCTV 는 근적외선을 이용하여 회색조 영상을 획득한다. 본 논문에서는 저조도에서 획득한 근적외선 영상을 이용한 물체 검출 및 GAN 을 통해 재구성된 컬러 영상에 생기는 컬러 잡음을 제거하는 방법을 제안한다. 기존의 재구성된 컬러 영상의 PSNR 측면에서 22.5dB 가 나왔으나, 영상 합성을 통해 컬러 노이즈를 제거한 영상의 PSNR 은 34dB 가 나왔다. 본 논문은 컬러 노이즈를 제거하면서 원래의 색의 유지가 제대로 이루어 졌는지는 주관적인 평가 방법을 통해 확인하였다.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.19 no.3
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• pp.129-136
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• 2020

In this paper, we propose a thermal imagery-based object detection algorithm for low-light level nighttime surveillance system. Many features selected by Haar-like feature selection algorithm and existing Adaboost algorithm are often vulnerable to noise and problems with similar or overlapping feature set for learning samples. It also removes noise from the feature set from the surveillance image of the low-light night environment, and implements it using the lightweight extended Haar feature and adaboost learning algorithm to enable fast and efficient real-time feature selection. Experiments use extended Haar feature points to recognize non-predictive objects with motion in nighttime low-light environments. The Adaboost learning algorithm with video frame 800*600 thermal image as input is implemented with CUDA 9.0 platform for simulation. As a result, the results of object detection confirmed that the success rate was about 90% or more, and the processing speed was about 30% faster than the computational results obtained through histogram equalization operations in general images.