• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.5
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• pp.1064-1070
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• 2012

Nowadays, image processing has been applied in a variety of fields. In order to preserve the high quality of visual the degradation phenomenon for images should be removed. Noise is one of the representative elements cause of the degradation phenomenon and AWGN(additive white Gaussian noise) always damages images. In this paper, an mixed filter algorithm, which is based on parallel denoising method, is proposed to suppress AWGN. This algorithm parallels the spatial domain wiener filter and the wavelet domain thresholding method which thresholding function is selected based on scale level. The proposed modified thresholding function which considers the dependency between parent and child coefficient performs well on suppressing noise.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.10a
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• pp.694-696
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• 2017

Image processing is being hailed as an important field with various applications as the digital information era advances. In particular, studies on methods to remove noise from images are being actively undertaken. This paper suggests an image restoration filter that processes through a weighted filter in accordance with the direction of partial masks to preserve edge while replacing the noise in the images with neighboring pixels. The PSNR(peak signal to noise ratio) was used as a tool to objectively judge the improvement effects compared to existing methods.

• Journal of Broadcast Engineering
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• v.26 no.7
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• pp.868-876
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• 2021

Deploying deep neural network model training performs remarkable performance in the fields of Object detection and Instance segmentation. To train these models, features are first extracted from the input image using a backbone network. The extracted features can be reused by various tasks. Research has been actively conducted to serve various tasks by using these learned features. In this process, standardization discussions about encoding, decoding, and transmission methods are proceeding actively. In this scenario, it is necessary to analyze the response characteristics of features against various distortions that may occur in the data transmission or data compression process. In this paper, experiment was conducted to inject various distortions into the feature in the object recognition task. And analyze the mAP (mean Average Precision) metric between the predicted value output from the neural network and the target value as the intensity of various distortions was increased. Experiments have shown that features are more robust to distortion than images. And this points out that using the feature as transmission means can prevent the loss of information against the various distortions during data transmission and compression process.

• Current Optics and Photonics
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• v.7 no.3
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• pp.283-296
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• 2023

An intraoral spectral domain optical coherence tomography (SD-OCT) system has been developed, using a custom-built hand-held scanner and spectrometer. The hand-held OCT probe, based on a microelectromechanical systems scanner and a self-built miniaturized drive circuit, had a field of view sufficient for dental diagnosis. The spectrometer using a fabricated f-theta lens provided the image depth required for dental diagnosis. The axial and transverse resolutions of the OCT system in air were 7.5 ㎛ and 12 ㎛ respectively. The hand-held probe could scan an area of 10 × 10 mm², and the spectrometer could image along a depth of 2.5 mm. To verify the utility of the developed OCT system, OCT images of tooth hard and soft tissues were acquired, and a user-interface program for diagnosis was developed. Early caries and microcracks that were difficult to diagnose with existing methods could be found, and the state of restoration could be observed. Measuring the depth of the gingival sulcus, distinguishing subgingival calculus, and detecting an implant under the gingiva suggested the possibility of the SD-OCT system as a diagnostic for dental soft tissues. Through the presented OCT images, the capability of the developed SD-OCT system for dental diagnosis was demonstrated.

• Applied Microscopy
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• v.50
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• pp.23.1-23.9
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• 2020

We propose an effective deep learning model to denoise scanning transmission electron microscopy (STEM) image series, named Noise2Atom, to map images from a source domain 𝓢 to a target domain 𝓒, where 𝓢 is for our noisy experimental dataset, and 𝓒 is for the desired clear atomic images. Noise2Atom uses two external networks to apply additional constraints from the domain knowledge. This model requires no signal prior, no noise model estimation, and no paired training images. The only assumption is that the inputs are acquired with identical experimental configurations. To evaluate the restoration performance of our model, as it is impossible to obtain ground truth for our experimental dataset, we propose consecutive structural similarity (CSS) for image quality assessment, based on the fact that the structures remain much the same as the previous frame(s) within small scan intervals. We demonstrate the superiority of our model by providing evaluation in terms of CSS and visual quality on different experimental datasets.

• The Journal of the Acoustical Society of Korea
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• v.37 no.1
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• pp.12-20
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• 2018

Efforts have been made to reconstruct low-resolution underwater images to high-resolution ones by using the image SR (Super-Resolution) method, all to improve efficiency when acquiring side-scan sonar images. As side-scan sonar images are similar with the optical images with respect to exploiting 2-dimensional signals, conventional image restoration methods for optical images can be considered as a solution. One of the most typical super-resolution methods for optical image is a sparse coding and there are studies for verifying applicability of sparse coding method for underwater images by analyzing sparsity of underwater images. Sparse coding is a method that obtains recovered signal from input signal by linear combination of dictionary and sparse coefficients. However, it requires huge computational load to accurately estimate sparse coefficients. In this study, a sparse coding based underwater image super-resolution method is applied while a selective reconstruction method for object region is suggested to reduce the processing time. For this method, this paper proposes an edge detection and object and non object region classification method for underwater images and combine it with sparse coding based image super-resolution method. Effectiveness of the proposed method is verified by reducing the processing time for image reconstruction over 32 % while preserving same level of PSNR (Peak Signal-to-Noise Ratio) compared with conventional method.

• Journal of Korea Multimedia Society
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• v.13 no.3
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• pp.438-456
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• 2010

Iris recognition uses a unique iris pattern of user to identify person. In order to enhance the performance of iris recognition, it is reported that the diameter of iris region should be greater than 200 pixels in the captured iris image. So, the previous iris system used zoom lens camera, which can increase the size and cost of system. To overcome these problems, we propose a new method of enhancing the accuracy of iris recognition on low-resolution iris images which are captured without a zoom lens. This research is novel in the following two ways compared to previous works. First, this research is the first one to analyze the performance degradation of iris recognition according to the decrease of the image resolution by excluding other factors such as image blurring and the occlusion of eyelid and eyelash. Second, in order to restore a high-resolution iris image from single low-resolution one, we propose a new method based on multiple multi-layered perceptrons (MLPs) which are trained according to the edge direction of iris patterns. From that, the accuracy of iris recognition with the restored images was much enhanced. Experimental results showed that when the iris images down-sampled by 6% compared to the original image were restored into the high resolution ones by using the proposed method, the EER of iris recognition was reduced as much as 0.133% (1.485% - 1.352%) in comparison with that by using bi-linear interpolation

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.237-240
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• 1996

본 논문에서는 방향성을 갖는 새로운 공간적응 조절연산자와 비선형필터를 이용한 제한 반복적 영상복원 알고리듬을 제안하고 제안한 알고리듬의 수렴성에 대하여 분석을 하고 있다. 일반적인 제한반복적 영상복원 기법에서는 열화된 영상을 복원하는 과정에서 에지 및 경계부분의 재번짐이 지나친 잡음성분의 증폭에 의한 고리현상 등이 발생한다. 이러한 문제들을 해결하기 위하여 본 논문에서는 다음과 같은 기법을 도입하고 있다. 첫째는, 방향성을 갖는 새로운 공간적응 조절연산자를 적용하여 에지 등의 재번짐을 막고 고주파수 영역의 복원성능을 개선하고 있다. 둘째로, 적응적인 비선형필터를 사용하여 잡음성분과 같은 고주파수 영역의 지나친 증폭에 따른 문제를 해결하고 있다. 그리고, 제안한 논문의 안정성과 수렴성을 보장하기 위한 조건을 분석하고 있다. 열화된 영상에 대하여 실험한 결과, 기존의 다른 결과보다 우수한 성능이 있었고, 특히, 에지의 복원성능 및 고리현상의 제거에 두드러진 특징을 나타내었다.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.30B no.9
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• pp.76-83
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• 1993

A morphological filtering algorithm using directional information is presented. Directional filtering technique is effective in reducing noises and preserving edges. The proposed directional filtering is composed of two stage filtering processes. The opening and closing operations in the lst stage are performed for the pixels is aligned to the vertical, horizontal, and two diagonal directions, respectively. The opening operation supresses the positive impulse noises, while the closing operation the negative ones. Then, each directional result and their average value are filtered by the opening or closing operations in the 2nd stage. The averaging operation diminishes the effects of Gaussian noises in the homogeneous regions. Thus, the morphological operation in the 1 st stageremoves the impulse noises and in 2nd stage reduces. Gaussian ones. The experimental results show that the proposed filtering is superior to the existing nonlinear filtering in the aspects of the subjective quality. Also, the morphological filtering method reduces the computational loads.

• Proceedings of the IEEK Conference
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• 2000.09a
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• pp.415-418
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• 2000

렌즈의 초점이 맞지 않아 흐려지고 잡음으로 훼손된 영상을 복원하는 경우에 일반적으로 정칙화 반복복원방법이 사용된다. 기존의 방법은 영상의 국부적인 특성을 고려하지 않고 영상전체에 일률적으로 정칙화를 행함으로써 윤곽부분에서는 리플잡음을 초래하고 평면부분에서도 잡음중폭을 피할 수 없으며, 또한 시각적으로 효율적이지 못한 면이 있다. 이러한 문제점을 개선하기 위하여, 본 논문에서는 영상을 방향이 없는 평면영역과 4가지 방향을 갖는 윤곽영역으로 나누어, 윤곽방향을 고려한 방향성 정칙화 연산자를 사용하여 평면영역과 윤곽영역의 방향특성에 따라 적응적으로 처리하는 반복복원방법을 제안한다. 제안한 방법은 기존의 방법과 비교하여 평면영역에서의 잡음 평활화가 개선되고 시각적으로 중요한 윤곽부분 복원에 효율적임을 실험결과를 통해 알 수 있었으며 ISNR 면에서도 우수하였다.