TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Adaptive High-order Variation De-noising Method for Edge Detection with Wavelet Coefficients |
| 영문제목(English Title) |
Adaptive High-order Variation De-noising Method for Edge Detection with Wavelet Coefficients |
| 저자(Author) |
Chenghua Liu
Anhong Wang
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| 원문수록처(Citation) |
VOL 17 NO. 02 PP. 0412 ~ 0434 (2023. 02) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
This study discusses the high-order diffusion method in the wavelet domain. It aims to improve the edge protection capability of the high-order diffusion method using wavelet coefficients that can reflect image information. During the first step of the proposed diffusion method, the wavelet packet decomposition is a more refined decomposition method that can extract the texture and structure information of the image at different resolution levels. The high-frequency wavelet coefficients are then used to construct the edge detection function. Subsequently, because accurate wavelet coefficients can more accurately reflect the edges and details of the image information, by introducing the idea of state weight, a scheme for recovering wavelet coefficients is proposed. Finally, the edge detection function is constructed by the module of the wavelet coefficients to guide high-order diffusion, the denoised image is obtained. The experimental results showed that the method presented in this study improves the denoising ability of the high-order diffusion model, and the edge protection index (SSIM) outperforms the main methods, including the block matching and 3D collaborative filtering (BM3D) and the deep learning-based image processing methods. For images with rich textural details, the present method improves the clarity of the obtained images and the completeness of the edges, demonstrating its advantages in denoising and edge protection. |
| 키워드(Keyword) |
Denoising
diffusion function
edge protection
high-order diffusion
state weight
wavelet transform
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| 파일첨부 |
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