• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.6
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• pp.311-317
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• 2011

In this paper, we proposed realtime image enhancing method on the three noise types of input images, such as haze, low contrast and back light images. Some conventional de-hazing algorithms have good performance but need large memories and high computational burdens. We proposed the efficient algorithm which not only removes the haze but also reduces memory usage and computational complexity. We implemented the realtime system by using DM6446 DSP chip, and it showed the excellent result in these three problems; haze, low contrast and back light. We implemented the system with the processing speed at 15 frames/sec.

• Proceedings of the Korean Society for Information Management Conference
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• 1994.12a
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• pp.7-10
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• 1994

우리나라 연구소의 소규모 검색체계에서는 시소러스를 이용하지 않고 검색체계를 유지해도 별 문제가 없다는 생각을 가져온 것이 사실이다. 그러나 현실적으로는 검색효율이 극히 저조하고 잡음율이 높을 뿐만 아니라 필요한 정보의 접근이 불가능한 경우까지도 있다. 그래서 과연 현 체계대로 검색했을때 검색효율 즉 적합율과 재현율은 어느 정도 인가를 실험을 통해서 측정해 보았더니 극히 저조하다는 결론을 얻었고 그 원인을 분석해 보았다.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.5
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• pp.601-608
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• 2014

In this study we propose an image denoising algorithm appropriate for mobile smart phone equipped with limited computing ability, which has better performance and at the same time comparable quality comparing with previous studies. The proposed image denoising algorithm for mobile smart phone cameras in low level light environment reduces computational complexity and also prevents edge smoothing by extracting just Gaussian noises from the noisy input image. According to the experiment result, we verified that our algorithm has much better PSNR value than methods applying mean filter or median filter. Also the result image from our algorithm has better clear quality since it preserves edges while smoothing input image. Moreover, the suggested algorithm reduces computational complexity about 52% compared to the method applying original Laplacian mask computation, and we verified that our algorithm has good denoising quality by implementing the algorithm in Android smart phone.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.169-171
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• 2013

최근 Full-HD TV, UHDTV의 보급에 따라 고화질 영상에 대한 수요가 증가하고 있으며 N-Screen 서비스의 확장으로 고화질 영상을 빠르게 전송하는 문제의 중요성은 더욱 커지고 있다. 고화질 영상을 빠르게 전송하기 위해서는 압축 효율의 향상이 필요한데, 일반적으로 영상에 잡음이 많을 때에는 압축 효율이 떨어진다. 본 논문에서는 다양한 원인의 잡음들 중에 film grain noise에 초점을 맞추어 이를 조절하여 영상압축의 효율을 높이는 방법을 연구한다. film grain은 영화촬영 방법 및 환경 등에 따라 강도가 달라지기도 하지만 필름으로 촬영한 모든 영화에서 쉽게 관찰할 수 있으며 앞으로도 계속 포함이 될 것으로 예상되고, 디지털 영화의 경우에도 저조도에서는 이와 비슷한 특성의 잡음이 발생한다. 재안하는 방법에서는 film grain이 포함된 영상에서 grain을 추출/제거한 영상을 압축하며 추출한 film grain에서 작은 영역을 선택하여 sample grain을 만든 후 별도로 압축한다. 디코더에서 grain을 없앤 영상만을 보여줄 수 있지만, 경우에 따라 grain이 없으면 심미적으로 오히려 좋지 않은 결과가 보이기도 한다. 따라서 압축을 푼 후에는 sample grain에서 원본 영상 크기의 grain을 합성한 후 grain을 제거한 영상과 더하여 grain이 포함된 영상을 재 생성한다. 실험한 결과 원본과 유사한 grain이 생성되면서 압축효율이 향상됨을 확인할 수 있다.

• The Journal of Korean Institute of Information Technology
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• v.16 no.11
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• pp.85-95
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• 2018

With the popularity of smartphones, most peoples have been using mobile cameras to capture photographs. However, due to insufficient amount of lights in a low lighting condition, unwanted noises can be generated during image acquisition. To remove the noise, a method of using deep convolutional neural networks is introduced. However, this method still lacks the ability to describe textures and edges, even though it has made significant progress in terms of visual quality performance. Therefore, in this paper, the HOG (Histogram of Oriented Gradients) images that contain information about edge orientations are used. More specifically, a method of learning deep convolutional neural networks is proposed by stacking noise and HOG images into an input tensor. Experiment results confirm that the proposed method not only can obtain excellent result in visual quality evaluations, compared to conventional methods, but also enable textures and edges to be improved visually.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.11
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• pp.1462-1467
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• 2018

A number of algorithms for improving the image quality of low light images have been studied using a single image or multiple exposure images. The low light image is low in contrast and has a large amount of noise, which limits the identification of information of the subject. This paper proposes the hardware design of algorithms that improve the quality of low light image using 2 multiple exposure images taken with a dual camera. The proposed hardware structure is designed in real time processing in a way that does not use frame memory and line memory using transfer function. The proposed hardware design has been designed using Verilog and validated in Modelsim. Finally, when the proposed algorithm is implemented on FPGA using xc7z045-2ffg900 as the target board, the maximum operating frequency is 167.617MHz. When the image size is 1920x1080, the total clock cycle time is 2,076,601 and can be processed in real time at 80.7fps.

• Proceedings of the Korea Information Processing Society Conference
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• 2022.05a
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• pp.443-445
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• 2022

광원 자체의 밝기가 낮거나 드리워진 그림자 등의 이유로 어두운 영역을 포함하고 있는 저조도 영상으로 인해 물체의 식별이 어려운 상황을 일상생활에서 겪게 된다. 본 논문에서는 조명 성분의 영향을 줄이고 객체의 특징을 표현하는 반사 성분을 강조하여 화질을 개선한다. 또한 촬영하는 카메라와 영상의 물체 사이의 상대적인 움직임으로 발생하는 흐릿한 영역을 최대한 제거해주고 잡음까지 보정이 되는 Stack-attention 기법을 제안한다.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.9
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• pp.127-135
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• 2013

In order to acquire images in low-light environments, it is usually necessary to adopt long exposure times or resort to flash lights. However, flashes often induce color distortion, cause the red-eye effect and can be disturbing to subjects. On the other hand, long-exposure shots are susceptible to subject-motion, as well as motion-blur due to camera shake when performed hand-held. A recently introduced technique to overcome the limitations of traditional low-light photography is that of multi-spectral flash. Multi-spectral flash images are a combination of UV/IR and visible spectrum information. The general idea is that of retrieving details from the UV/IR spectrum and color from the visible spectrum. However, multi-spectral flash images themselves are subject to color distortion and noise. This works presents a method to compute multi-spectral flash images so that noise can be reduced and color accuracy improved. The proposed approach is a previously seen optimization method, improved by the introduction of a weight map used to discriminate uniform regions from detail regions. The weight map is generated by applying canny edge operator and it is applied to the optimization process for discriminating the weights in uniform region and edge. Accordingly, the weight of color information is increased in the uniform region and the detail region of weight is decreased in detail region. Therefore, the proposed method can be enhancing color reproduction and removing artifacts. The performance of the proposed method has been objectively evaluated using long-exposure shots as reference.

• The KIPS Transactions:PartD
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• v.13D no.1 s.104
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• pp.125-132
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• 2006

The existing recognition system of license plates cannot get the satisfactory result in noise environments. The purpose of this paper is to propose an algorithm that can recognize the region of license plates accurately in a noise environment. The algorithm is formulated by reorganizing the U- and V-channels of YUV color space as YUV is insensitive to light and carries less data than RGB color information. The region of license plates has been extracted by the geometric characteristics, sizes, and places of labeling images. The proposed algorithm was found to improve the process of extracting the region of license plates in various noise environments.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.273-285
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• 2007

Most stereo matching methods use intensity values in small image patches to measure the correspondence between two points. If the noisy pixels are used in computing the corresponding point, the matching performance becomes low. For this reason, the noise plays a critical role in determining the matching performance. In this paper, we propose a method for combining intensity and edge filters robust to the noise in order to improve the performance of stereo matching using high resolution satellite imagery. We used intensity filters such as Mean, Median, Midpoint and Gaussian filter and edge filters such as Gradient, Roberts, Prewitt, Sobel and Laplacian filter. To evaluate the performance of intensity and edge filters, experiments were carried out on both synthetic images and satellite images with uniform or gaussian noise. Then each filter was ranked based on its performance. Among the intensity and edge filters, Median and Sobel filter showed best performance while Midpoint and Laplacian filter showed worst result. We used Ikonos satellite stereo imagery in the experiments and the matching method using Median and Sobel filter showed better matching results than other filter combinations.