ORTHOGONAL PROJECTION TRANSFORM COMPRESSION OF SPATIALLY INVARIANT IMAGE SEQUENCES WITH POISSON NOISE

摘要

Many important imaging applications in medical imaging, remote sensing and other areas result in a set of images of the same scene with no relative scene-sensor motion and in which the pixel intensities are the linear sum of the contributions of the distinct features in the scene. Such image sets are called linearly additive spatially invariant (LA SI) image sequences. Previous research has shown, both mathematically and with examples, that a K-image sequence with M distinct features (M < K) can be compressed using either the KL transform or the orthogonal projection (OP) transform into an M-image set from which the original K-image set can be recovered with complete feature reconstruction. The imaging noise in those applications is typically distributed uniformly over the image scene independent of the features. This paper uses a synthetic image set with Poisson noise to investigate LA SI imaging applications, such as nuclear medicine and electron microscopy, where the imaging noise is Poisson distributed and, therefore, is related to the feature values. It is shown here that, even with feature-related Poisson noise, perfect (zero mean error) feature reconstruction with noise reduction is achieved with the OP transform.