.png){kind=icon}
.png){kind=icon}
Return
Dictionary-based anisotropic diffusion for noise reduction
.png){kind=icon}
PRE
AI
DOI：10.1016/j.patrec.2014.05.003.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents an anisotropic diffusion-based approach to noise reduction, which utilizes a pre-trained dictionary for diffusivity determination. The proposed method involves off-line and on-line processing steps. For off-line processing, a multiscale region analysis that effectively separates the structure information from image noise is proposed. Using multiscale region analysis, the proposed approach classifies local regions and constructs a dictionary of several patch classes. Further, this paper presents a dictionary-based diffusivity determination that exhibits enhanced performance of anisotropic diffusion. In addition, we propose a single-pass adaptive smoothing that uses a diffusion path-based kernel, which is derived from iterative anisotropic diffusion operations. By using single-pass adaptive smoothing for both off-line and on-line processing, the proposed method is able to avoid the use of expensive iterative region analysis. In on-line processing, the proposed approach classifies input image patches using multiscale region analysis. It subsequently selects the diffusion threshold with the highest matching ratio from the dictionary for each region. Finally, single-pass adaptive smoothing is performed with the selected diffusion threshold. Simulations show that the proposed method outperforms benchmark methods by significantly enhancing the peak signal-to-noise ratio and structural similarity indexes. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Image denoising
Noise reduction
Anisotropic diffusion
Multiscale region analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.9K
Citations:
1.6W