• Journal of Information Processing Systems
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• 제14권2호
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• pp.552-562
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• 2018

With the advent of the information society, image restoration technology has aroused considerable interest. Guided image filtering is more effective in suppressing noise in homogeneous regions, but its edge-preserving property is poor. As such, the critical part of guided filtering lies in the selection of the guided image. The result of the Expected Patch Log Likelihood (EPLL) method maintains a good structure, but it is easy to produce the ladder effect in homogeneous areas. According to the complementarity of EPLL with guided filtering, we propose a method of coupling EPLL and guided filtering for image de-noising. The EPLL model is adopted to construct the guided image for the guided filtering, which can provide better structural information for the guided filtering. Meanwhile, with the secondary smoothing of guided image filtering in image homogenization areas, we can improve the noise suppression effect in those areas while reducing the ladder effect brought about by the EPLL. The experimental results show that it not only retains the excellent performance of EPLL, but also produces better visual effects and a higher peak signal-to-noise ratio by adopting the proposed method.

• International journal of advanced smart convergence
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• 제9권3호
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• pp.85-96
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• 2020

It is important to extract salient object image and to solve the invariance problem for image recognition. In this paper we propose a new hybrid algorithm for invariance and improved classification performance in image recognition, whose algorithm is combined by FT(Frequency-tuned Salient Region Detection) algorithm, Guided filter, Zernike moments, and a simple artificial neural network (Multi-layer Perceptron). The conventional FT algorithm is used to extract initial salient object image, the guided filtering to preserve edge details, Zernike moments to solve invariance problem, and a classification to recognize the extracted image. For guided filtering, guided filter is used, and Multi-layer Perceptron which is a simple artificial neural networks is introduced for classification. Experimental results show that this algorithm can achieve a superior performance in the process of extracting salient object image and invariant moment feature. And the results show that the algorithm can also classifies the extracted object image with improved recognition rate.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.417-426
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• 2023

To improve the effect of image restoration and solve the image detail loss, an image dehazing enhancement algorithm based on mean guided filtering is proposed. The superpixel calculation method is used to pre-segment the original foggy image to obtain different sub-regions. The Ncut algorithm is used to segment the original image, and it outputs the segmented image until there is no more region merging in the image. By means of the mean-guided filtering method, the minimum value is selected as the value of the current pixel point in the local small block of the dark image, and the dark primary color image is obtained, and its transmittance is calculated to obtain the image edge detection result. According to the prior law of dark channel, a classic image dehazing enhancement model is established, and the model is combined with a median filter with low computational complexity to denoise the image in real time and maintain the jump of the mutation area to achieve image dehazing enhancement. The experimental results show that the image dehazing and enhancement effect of the proposed algorithm has obvious advantages, can retain a large amount of image detail information, and the values of information entropy, peak signal-to-noise ratio, and structural similarity are high. The research innovatively combines a variety of methods to achieve image dehazing and improve the quality effect. Through segmentation, filtering, denoising and other operations, the image quality is effectively improved, which provides an important reference for the improvement of image processing technology.

• 한국전자통신학회논문지
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• 제16권6호
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• pp.1303-1310
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• 2021

본 논문에서는 안개 및 스모그 등의 조건에 의해 열화된 실외영상의 화질을 개선하기 위하여 에지 근처에서 패치(patch) 단위 및 픽셀 단위의 dark channel을 비교하여 에지 정보를 보존하는 전달량 추정 방법을 제안한다. 또한 영상의 객체와 배경의 자연스러운 복원을 위하여 라플라시안 연산을 이용한 에지 정보에 Guided Image Filtering (GIF)을 적용하는 정련 과정을 통해 효과적인 단일 영상 기반 안개 제거 방법을 제안한다. 안개가 포함된 다양한 실외영상에 대해 수행한 실험 결과는 제안한 방법이 기존의 방법에 비해 적은 계산 복잡도를 갖는 동시에 후광효과와 같은 왜곡이 감소하고 우수한 안개 제거 성능을 보여 실시간성이 요구되는 기기를 포함한 다양한 분야에 적용될 수 있음을 확인할 수 있다.

• 디지털콘텐츠학회 논문지
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• 제17권6호
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• pp.523-529
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• 2016

스테레오 매칭 알고리즘은 컴퓨터 비전과 사진촬영에 필수적인 알고리즘으로 정확도와 복잡도는 스테레오 매칭 알고리즘의 주요 문제점이었다. 그 중에서도 복잡도가 높은 문제점을 극복하기 위해 비용 볼륨 필터링을 기반한 스테레오 매칭 알고리즘에 대한 많은 연구가 이루어졌다. 지역 스테레오 매칭 기술인 유도 영상 필터링 기술은 O(N)의 복잡도를 가지고 있지만, 여전히 실시간 3D 비디오 서비스를 제공하기에는 계산량이 많은 편이다. 따라서 본 논문에서 고속 유도 영상 필터링에 기반한 스테레오 매칭 알고리즘을 제안한다. 고속 유도 필터링은 서브샘플 비율 $\small{s}$에 따라 복잡도 $O(N/\small{s}^2)$을 가지는 알고리즘이다. 제안하는 알고리즘은 효과적인 스테레오 알고리즘을 성능을 보여줌과 동시에 3D 서비스를 위한 빠른 실행 시간을 보여준다.

• 설비공학논문집
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• 제27권3호
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• pp.174-178
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• 2015

This paper describes a signal processing technique for identifying signals from defects by using an electromagnetic-ultrasonic guided waves method based on a magnetostrictive sensor that generates a torsional mode (T(0, 1)). Because this technique is based on the digital filtering, the filtered signals provide information on the relationship between the cutoff frequency of band-pass filter and the characteristic of defect signals in heat exchange tubes. To verify the performance of the technique, artificial defects with various thickness reduction ration and shape were machined in titanium tubes, and digital filtering results are reported. The results show that digital filtering provides information to the identify shape of defects and the contact condition between the tube and support ring. Therefore, the proposed technique has good potential as a tool for evaluating the integrity of heat exchange tubes.

• 한국컴퓨터정보학회논문지
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• 제21권11호
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• pp.23-30
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• 2016

In this paper, we propose an adaptive histogram projection technique for dynamic range compression and an efficient detail enhancement method which is enhancing strong edge while reducing noise. First, The high dynamic range image is divided into low-pass component and high-pass component by applying 'guided image filtering'. After applying 'guided filter' to high dynamic range image, second, the low-pass component of the image is compressed into 8-bit with the adaptive histogram projection technique which is using global standard deviation value of whole image. Third, the high-pass component of the image adaptively reduces noise and intensifies the strong edges using standard deviation value in local path of the guided filter. Lastly, the monitor display image is summed up with the compressed low-pass component and the edge-intensified high-pass component. At the end of this paper, the experimental result show that the suggested technique can be applied properly to the IR images of various scenes.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.27-33
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• 2018

In this paper, we propose an efficient edge preserving smoothing filter for Infrared image that can reduce noise while preserving edge information. Infrared images suffer from low signal-to-noise ratio, low edge detail information and low contrast. So, detail enhancement and noise reduction play crucial roles in infrared image processing. We first apply a guided image filter as a local analysis. After the filtering process, we optimization globally using relativity of guided image filter. Our method outperforms the previous methods in removing the noise while preserving edge information and detail enhancement.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권4호
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• pp.1846-1863
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• 2016

High dynamic range (HDR) images can present the perfect real scene and rich color information. A commonly encountered problem in practical applications is how to well visualize HDR images on standard display devices. In this paper, we propose a multi-scale decomposition method using guided filtering for HDR image tone mapping. In our algorithm, HDR images are directly decomposed into three layers：base layer, coarse scale detail layer and fine detail layer. We propose an effective function to compress the base layer and the coarse scale detail layer. An adaptive function is also proposed for detail adjustment. Experimental results show that the proposed algorithm effectively accomplishes dynamic range compression and maintains good global contrast as well as local contrast. It also presents more image details and keeps high color saturation.

• Journal of Information Processing Systems
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• 제19권4호
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• pp.465-473
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• 2023

An image enhancement algorithm and image defogging method are studied in this paper. The formation of fog and the characteristics of fog image are analyzed, and the fog image is preprocessed by histogram equalization method; then the additive white noise is removed by foggy image attenuation model, the atmospheric scattering physical model is constructed, the image detail characteristics are enhanced by image enhancement method, and the visual effect of defogging image is enhanced by guided filtering method. The proposed method has a good defogging effect on the image. When the number of training iterations is 3,000, the peak signal-to-noise ratio of the proposed method is 43.29 dB and the image structure similarity is 0.9616, indicating excellent image defogging effect.