Image feature and noise detection based on statistical hypothesis tests and their applications in noise reduction

In many video processing applications in the field of consumer electronics such as digital TV, it is well understood that the presence of a noise limits the performance of video enhancement functions due to the time-varying characteristics of the noise. The basic difficulty is that the noise and the signal are difficult to be distinguished. This paper proposes image feature and noise detection algorithms, which effectively distinguish the noise from the image feature or vice versa. Specifically, the proposed algorithms provide a way of measuring the degree of noise with respect to the degree of image feature. The fundamental idea behind the proposed algorithms is to derive a statistical measure to estimate the fact that a noise has a random characteristic whereas an image feature has a spatial correlation among the associated neighbor samples. With the proposed algorithms, many video enhancement algorithms such as noise reduction or sharpness enhancement can be adaptively performed although a time varying noise is presented.