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http://dx.doi.org/10.5573/ieie.2014.51.8.087

Directionally Adaptive Aliasing and Noise Removal Using Dictionary Learning and Space-Frequency Analysis  

Chae, Eunjung (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Lee, Eunsung (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Cheong, Hejin (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Paik, Joonki (Dept. of Image Engineering, Graduate School of Advanced Image Science, Multimedia, and Film, Chung-Ang University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.51, no.8, 2014 , pp. 87-96 More about this Journal
Abstract
In this paper, we propose a directionally adaptive aliasing and noise removal using dictionary learning based on space-frequency analysis. The proposed aliasing and noise removal algorithm consists of two modules; i) aliasing and noise detection using dictionary learning and analysis of frequency characteristics from the combined wavelet-Fourier transform and ii) aliasing removal with suppressing noise based on the directional shrinkage in the detected regions. The proposed method can preserve the high-frequency details because aliasing and noise region is detected. Experimental results show that the proposed algorithm can efficiently reduce aliasing and noise while minimizing losses of high-frequency details and generation of artifacts comparing with the conventional methods. The proposed algorithm is suitable for various applications such as image resampling, super-resolution image, and robot vision.
Keywords
안티에일리어싱;잡음제거;웨이블릿-푸리에 변환;방향 적응적 계수 축소;사전 학습;
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