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Comparison of Edge Detection of Images with Various Types of Noise and Denoising Methods
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잡음 종류와 잡음 제거방식에 따른 영상의 윤곽선 검출 비교

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Type
Academic journal
Author
Jieun Kim (연세대학교) Deokwoo Lee (계명대학교)
Journal
Korea Multimedia Society Journal of Korea Multimedia Society Vol.26 No.4 KCI Accredited Journals
Published
2023.4
Pages
533 - 541 (9page)
DOI
10.9717/kmms.2023.26.4.533

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Comparison of Edge Detection of Images with Various Types of Noise and Denoising Methods
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Abstract· Keywords

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In this paper, we show comparison results of edge detection of images that have additive gaussian noise or salt and pepper noise by using various techniques of noise removal such as filtering, morphology and deep learning based ones. In particular, this present work provides comparison results of noise removal by using gaussian filter, open and close operations of morphology and auto-encoder model followed by carrying out edge detection. Robert cross, Sobel, Prewitt and Canny detectors are used for edge detection of the images with noise removal. Experimental results show that noise removal results are different with characteristics of noise and techniques applied for noise removal. In addition, deep learning based technique, auto-encoder does not always shows superior results of noise removal, particularly in the case of existence of salt-pepper noise. In the experiments, gaussian noise or salt-pepper noise is used and peak signal noise ratio (PSNR) is used for quantitative comparison and the results of edge detection is qualitatively compared from visual perspective.
#Edge Detection #Noise Removal #Low Pass Filter #Morphology #Auto-Encoder

Contents

ABSTRACT
1. 서론
2. 영상의 잡음 제거
3. 윤곽선 검출
4. 실험 결과
5. 결론
REFERENCE

References (14)

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H. Alao, J.-S. Kim, T. Kim, and K. Lee, “Efficient Multi-Scalable Network for Single Image Super Resolution,” Journal of Multimedia Information System, Vol. 8, No. 2, pp. 101-110, 2021. google schola S. Kim and Y. Jung, “Deep Network for Detail Enhancement in Image Denoising,” Journal of Korea Multimedia Society, Vol. 22, No. 6, pp. 646-654, 2019. google schola S. Kim, J. Lee, S.-H. Lee, and D. Jun, “Image Restoration Method using Denoising CNN,” Journal of Korea Multimedia Society, Vol. 25, No. 1, pp. 29-38, 2022. google schola J.I. Rosa, O. Gutiérrez, J. Villa-Hernández, G. Moreno, E. Gonzalez, and D. Alaniz, “Entropy Estimation for Robust Image Segmentation in Presence of Non Gaussian Noise,” Multimedia Tools and Applications, Vol. 80, pp. 6991-7021, 2021. google schola R. Gonzalez and R. Woods, Digital Image Processing (4th Edition), Pearson, 2017. google schola

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