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Training-Based Noise Reduction Method Considering Noise Correlation for Visual Quality Improvement of Recorded Analog Video  

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.6, 2010 , pp. 28-38 More about this Journal
Abstract
In order to remove the noise contained in recorded analog video, it is important to recognize the real characteristics and strength of the noise. This paper presents an efficient training-based noise reduction method for recorded analog video after analyzing the noise characteristics of analog video captured in a real broadcasting system. First we show that there is non-negligible noise correlation in recorded analog video and describe the limitations of the traditional noise estimation and reduction methods based on additive white Gaussian noise (AWGN) model. In addition, we show that auto-regressive (AR) model considering noise correlation can be successfully utilized to estimate and synthesize the noise contained in the recorded analog video, and the estimated AR parameters are utilized in the training-based noise reduction scheme to reduce the video noise. Experiment results show that the proposed method can be efficiently applied for noise reduction of recorded analog video with non-negligible noise correlation.
Keywords
analog noise reduction; trained filter; auto-regressive model;
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