Image analysis: reconstructing the true scene from noisy images

Inspired by the principle of image restoration, a strategy for restoring the true scene from a given image has been developed. When the embedded model of a type of images is derived, the true scene can be recovered by seeking an image which best fits this model. Gaussian mixture and asymptotic independence of a pixel intensifies of x-ray CT and MR images have been proved which validate the use of independent Finite Normal Mixture (FNM) and locally dependent Markov random field (MRF) models. FNM is futher shown to be a degenerate Hidding MRF (HMRF). A two-level image analysis method is developed for recovering the true scene from the given x-ray CT and MR image. At the low-level, it is a pixel-based intensity processing method and utilizes the FNM model and an Expectation-Maximization algorithm, known as FNM-EM operation. At the high-level, it is a region-based context processing method and utilizes MRF and an Iterated Conditional Mode algorithm, known as MRF-ICM operation. The results obtained by applying this method to simulated and real phantom images demonstrated considerable success: the restored true scene is the ground truth of the objets in the given image. Results also demonstrated that best fitting the given image and best fitting the embedded model may lead to two different scenes, and only in the case of high SNR, these two scenes are closer.