A new adaptive PCA scheme for noise removal in image processing

The research reported in the paper focused on the development of a new adaptive scheme based on the use of principal directions (CSPCA). The proposed method is based exclusively on the information extracted form a series of noisy images that share the same statistical properties. Basically, the idea is that being given a signal corrupted by additive Gaussian noise, a soft shrinkage of the sparse components can be used to reduce the noise. In our CSPCA algorithm a shrinkage step is applied in the transformed space. A new variant of CSPCA noise removal algorithm is considered yielding to an adaptive learning technique. A series of comments concerning the experimental results are presented in the final section of the paper.