Adaptive noise reduction algorithms based on statistical hypotheses tests

In many video processing applications, the presence of a random noise is troublesome since most video enhancement functions produce visual artifacts if a priori of the noise is incorrect. The basic difficulty is that the noise and the signal are difficult to be distinguished. It was shown that the noise and image feature detection problem can be converted to statistical hypotheses tests based on the sample correlation in different orientations. In this paper, to further elaborate these hypotheses, we propose parametric, semi- parametric, and nonparametric statistical tests by combining with adaptive median filters. The proposed algorithms provide ways of measuring the degree of noise with respect to the degree of image feature, and the proposed adaptive noise reduction filtering framework provides good performance when the underlying noises are from Gaussian or non-Gaussian distributions. Simulation results for noise reduction show that the Bartlett and the Levene tests perform better regardless of the noise characteristics. Applications of the proposed algorithms can be found in digital TV, camcorders, digital cameras, and DVD players.