• 제목/요약/키워드: Image Reduction

검색결과 1,462건 처리시간 0.227초

Ventricle Image Restoration and Enhancement with Multi-thresholding and Multi-Filtering

  • Ryu, Kwang-Ryol;Jung, Eun-Suk
    • Journal of information and communication convergence engineering
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    • 제7권2호
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    • pp.231-234
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    • 2009
  • Speckle noise reduction for power Doppler ventricle coherent image for restoration and enhancement using Fast Wavelet Transform with multi-thresholding and multi-filtering on the each subbands is presented. Fast Wavelet Transform divides into low frequency component image to high frequency component image to be multi-resolved. Speckle noise is located on high frequency component in multi-resolution image mainly. A Doppler ventricle image is transformed and inversed with separated threshold function and filtering from low to high resolved images for restoration to utilize visualization for ventricle diagnosis. The experimental result shows that the proposed method has better performance in comparison with the conventional method.

비모수 베이지안 방법을 이용한 영상 잡음 제거 알고리즘 (Noise reduction algorithm for an image using nonparametric Bayesian method)

  • 우호영;김영화
    • 응용통계연구
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    • 제31권5호
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    • pp.555-572
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    • 2018
  • 영상처리 분야의 중요한 주제인 영상의 잡음 제거 과정은 원래의 순수한 영상이 다양한 원인으로 발생한 잡음에 의해 오염되었을때 이 잡음을 제거하거나 줄이는 것을 의미한다. 잡음 제거 과정에서는 영상에 추가된 잡음과 원 영상이 가진 고유한 특징들을 구별해내는 것이 중요하며 이에 대한 많은 연구가 진행되고 있다. 적응적 필터와 시그마 필터는 잡음 제거를 위하여 사용하는 대표적인 잡음 제거 필터이며 이 필터들의 효용성은 정확한 잡음 추정에 영향을 받는다. 따라서 본 연구에서는 디리클레 정규 혼합모형을 토대로 영상을 오염시키고 있는 잡음의 분포를 생성하고 이를 토대로 영상의 특징과 잡음을 구별하기 위한 베이지안 방법을 제시한다. 특히 잡음의 분포와 특징의 분포를 구별하기 위해 베이지안 추론을 전개하고 영상에 포함된 잡음을 제거하는 알고리즘을 제시하고자 한다.

비모수 방법을 사용한 영상 잡음 제거 알고리즘 (Image noise reduction algorithms using nonparametric method)

  • 우호영;김영화
    • 응용통계연구
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    • 제32권5호
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    • pp.721-740
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    • 2019
  • 영상처리 분야에서 중요한 분야인 잡음 제거는 통계적인 접근이 필요하지만 잡음에 대한 특정한 분포를 가정하기 어려우며 지역적 특징을 반영하는 공간 필터는 소표본에 해당하므로 모수적인 방법으로 접근할 수 없다. 1차 영상 미분과 2차 영상 미분은 영상에 포함된 잡음 수준에 따라 확연한 차이를 보이며 캐니 에지 검출기를 사용하면 보다 명확히 알 수 있다. 잡음 수준을 통계적으로 확인하고자 Fligner-Killeen 검정을 진행하고 붓스트랩 방법을 사용하였으며 추정된 잡음의 수준을 베타분포의 누적분포함수를 이용하여 0과 1사이의 값을 갖도록 하였다. 본 연구에서는 영상에 포함된 잡음 수준을 고려하는 잡음 제거 알고리즘을 제시하고자 한다.

STFT 기반 영상분석을 이용한 효과적인 잡음제거 알고리즘 (Effective Noise Reduction using STFT-based Content Analysis)

  • 백승인;정수웅;최종수;이상근
    • 전자공학회논문지
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    • 제52권4호
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    • pp.145-155
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    • 2015
  • 디지털 영상 처리 분야에서 잡음 제거는 활발히 연구되어오고 있으며, 최근에는 블록 기반의 잡음 제거 알고리즘이 널리 사용되고 있다. 저계수행렬 근사 기반의 잡음 제거 알고리즘은 WNNM(Weighted Nuclear Norm Minimization)과 블록 기반의 잡음 제거 방법을 적용하여 잡음 제거 방법에 대한 잠재력을 입증했다. 그러나 저계수행렬 근사 기반의 잡음 제거 알고리즘은 영상복원 과정에서 의도치 않은 아티팩트를 발생시킨다. 본 논문에서는 STFT(Short Time Fourier Transform)을 이용해 영상을 분석하여 기존 알고리즘에서 발생하는 아티팩트를 적응적으로 최소화시키는 방법을 제안한다. 성능을 확인하기 위해 다양한 잡음정도를 포함하는 영상에서 실험하였으며, 비교를 통해 제안된 방법이 기존의 잡음 제거 알고리즘보다 효과적으로 잡음을 제거하는 것을 확인했다.

X선 진단시 피폭선량을 반으로 줄이기 위한 Cu Filter의 두께 (The thickness of Cu Filter to reduce 1/2 of the patient dose)

  • 김정민;김성철
    • 대한방사선기술학회지:방사선기술과학
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    • 제24권1호
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    • pp.17-22
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    • 2001
  • Medical X-ray examination are increased double for the last $6{\sim}8$ years. Therefore a patient exposure dose should be decrease half every 7 years. We made an experiment on copper filter thickness to decrease a patient exposure dose up to half and compared to the Image quality by MTF. The results as follow 1. A thin region like extremities needs a thicker Cu filter as compared a thick region. 2. 1/2 reduction filter must be thicker when kVp Increase. 3. Exposure factor should be increas when using 1/2 reduction filter ; extremity is 4.0 times, chest 2.9 times, skull 1.62 times, and abdomen 1.58 times 4. The MTF of using 1/2 reduction filter is lower than without filter. But no difference of visual image. 5. 1/2 reduction filter compared with double speed screen showed almost same image quality.

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Noise Reduction using Fuzzy Mathematical Morphology

  • Kikuchi, Takuo;Nakatsuyama, Mikio;Murakam, Shuta
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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    • pp.745-749
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    • 1998
  • Mathematical morphology (MM) has been introduced as a powerful tool for studying the geometrical properties of images, MM is a good approach to digital image processing , which is based on the shape feature. The MM operators such as dilation, erosion, closing and opening have been applied successfully to image noise reduction. The MM filters can easily filter the noise when the noise factors are known. However it is very difficult to reduce the noise when images are ambiguous, because the boundary between the noise and object is vague. In this paper, we propose a new method to reduce noise from ambiguous images by using Fuzzy Mathematical Morphology (FMM) operators. Performance evaluation via simulations show that the FMM filters efficiently reduce the image noise. Furthermore, the FMM filters show a good performance compared with the conventional filters.

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공기윤활평판실험에서 디지털 영상분석을 통한 윤활공기막 검출기법 (A Study on the Lubricating Air-layer Detection Techniques with Digital Image Analysis in Flat Plate Air Lubrication Test)

  • 박성현;이인원
    • 한국가시화정보학회지
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    • 제14권1호
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    • pp.27-32
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    • 2016
  • The reduction of $CO_2$ emissions has been a key target in the Marine Industry since the IMO's Marine Environment Protection Committee published its findings in 2009. The representative emission index is termed as the EEDI (Energy Efficiency Design Index) for the new ships. Among various flow control techniques ever proposed, the air lubrication method is the one of most promising one in terms of practical applicability. The present study examines the basic characteristics of the flat plate test with intention of applying the air lubrication technology to the reduction of the resistance of a ship. Image analysis technique is proposed as a tool to quantify the effectiveness of the air lubrication method.

Speckle Noise Reduction with Morphological Adaptive Median Filtering Based on Edge Preservation

  • Jung, Eun Suk;Ryu, Conan K.R.;Hur, Chang Wu;Sun, Mingui
    • 한국정보통신학회:학술대회논문집
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    • 한국해양정보통신학회 2009년도 추계학술대회
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    • pp.329-332
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    • 2009
  • Speckle noise reduction for ultrasound CT image using morphological adaptive median filtering based on edge preservation is presented in this paper. Speckle noise is multiplicative feature and causes ultrasound image to degrade widely from transducer. An input image is classified into edge region and homogeneous region in preprocessing. The speckle is reduced by morphological operation on the 2D gray scale by using convolution and correlation, and edges are preserved. The adaptive median is processed to reduce an impulse noise. As the result the proposed method enhances the image to about 20% in comparison with Winer filter by Edge Preservation Index and PSNR.

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Speckle Noise Reduction for 3D Power Doppler Ventricle Image Restoration Using Wavelet Packet Transform

  • Jung, Eun-sug;Ryu, Conan K.R.;Hur, Chang Wu;Sun, Mingui
    • 한국정보통신학회:학술대회논문집
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    • 한국해양정보통신학회 2009년도 춘계학술대회
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    • pp.156-159
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    • 2009
  • Speckle noise reduction for 3D power doppler ventricle coherent image for restoration and enhancement using wavelet packet transform with separated thresholding is presented. Wavelet Packet Transform divide into low frequency component image to high frequency component image to be multi-resolved. speckle noise is located on high frequency component in multiresolution image mainly. A ventricle image is transformed and inversed with separated threshold function from low to high resolved images for restoration to be utilize visualization for ventricle diagnosis. The experimental result shows that the proposed method has better performance in comparison with the conventional method.

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압축 왜곡 감소를 위한 CNN 기반 이미지 화질개선 알고리즘 (CNN based Image Restoration Method for the Reduction of Compression Artifacts)

  • 이유호;전동산
    • 한국멀티미디어학회논문지
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    • 제25권5호
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    • pp.676-684
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    • 2022
  • As realistic media are widespread in various image processing areas, image or video compression is one of the key technologies to enable real-time applications with limited network bandwidth. Generally, image or video compression cause the unnecessary compression artifacts, such as blocking artifacts and ringing effects. In this study, we propose a Deep Residual Channel-attention Network, so called DRCAN, which consists of an input layer, a feature extractor and an output layer. Experimental results showed that the proposed DRCAN can reduced the total memory size and the inference time by as low as 47% and 59%, respectively. In addition, DRCAN can achieve a better peak signal-to-noise ratio and structural similarity index measure for compressed images compared to the previous methods.