Name
Abstract | ||
|---|---|---|
Images are often corrupted by impulse noise in the procedures of image acquisition and transmission. In this paper, we propose an efficient denoising scheme and its VLSI architecture for the removal of random-valued impulse noise. To achieve the goal of low cost, a low-complexity VLSI architecture is proposed. We employ a decision-tree-based impulse noise detector to detect the noisy pixels, and an edge-preserving filter to reconstruct the intensity values of noisy pixels. Furthermore, an adaptive technology is used to enhance the effects of removal of impulse noise. Our extensive experimental results demonstrate that the proposed technique can obtain better performances in terms of both quantitative evaluation and visual quality than the previous lower complexity methods. Moreover, the performance can be comparable to the higher complexity methods. The VLSI architecture of our design yields a processing rate of about 200 MHz by using TSMC 0.18 μm technology. Compared with the state-of-the-art techniques, this work can reduce memory storage by more than 99 percent. The design requires only low computational complexity and two line memory buffers. Its hardware cost is low and suitable to be applied to many real-time applications. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/TC.2011.256 | IEEE Trans. Computers |
Keywords | Field | DocType |
higher complexity method,efficient denoising architecture,random-valued impulse noise,previous lower complexity method,decision-tree-based impulse noise detector,noisy pixel,vlsi architecture,low cost,low computational complexity,impulse noise,low-complexity vlsi architecture,detectors,computational complexity,image reconstruction,vlsi,noise measurement,decision trees,noise,noise reduction,architecture,computer architecture,very large scale integration | Noise reduction,Noise measurement,Computer science,Real-time computing,Electronic engineering,Artificial intelligence,Impulse noise,Detector,Very-large-scale integration,Iterative reconstruction,Computer vision,Pixel,Computational complexity theory | Journal |
Volume | Issue | ISSN |
62 | 4 | 0018-9340 |
Citations | PageRank | References |
5 | 0.40 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chih-Yuan Lien | 1 | 91 | 9.64 |
| Chien-Chuan Huang | 2 | 54 | 4.98 |
| Pei-Yin Chen | 3 | 314 | 38.47 |
| Yi-Fan Lin | 4 | 5 | 0.40 |