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Motion Adaptive Temporal Noise Reduction Filtering Based on Iterative Least-Square Training  

Kim, Sung-Deuk (Dept. of IT & Electronics Education, Andong National University)
Lim, Kyoung-Won (Digital TV Lab., LG Electronics, Inc.)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.47, no.5, 2010 , pp. 127-135 More about this Journal
Abstract
In motion adaptive temporal noise reduction filtering used for reducing video noises, the strength of motion adaptive temporal filtering should be carefully controlled according to temporal movement. This paper presents a motion adaptive temporal filtering scheme based on least-square training. Each pixel is classified to a specific class code according to temporal movement, and then, an iterative least-square training method is applied for each class code to find optimal filtering coefficients. The iterative least-square training is an off-line procedure, and the trained filter coefficients are stored in a lookup table (LUT). In actual noise reduction filtering operation, after each pixel is classified by temporal movement, simple filtering operation is applied with the filter coefficients stored in the LUT according to the class code. Experiment results show that the proposed method efficiently reduces video noises without introducing blurring.
Keywords
noise reduction; temporal filter; least-square training;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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