Learning Adaptive and Sparse Representations of Medical Images

Abstract. In this paper we discuss the impact of using algorithms for dictionary learning to build adaptive and sparse representations of medi-cal images. The effectiveness of coding data as sparse linear combinations of the elements of an over-complete dictionary is well assessed in the med-ical context. Confirming what has been observed for natural images, we show the benefits of using adaptive dictionaries, directly learned from a set of training images, that better capture the distribution of the data. The experiments focus on the specific task of image denoising and pro-duce clear evidence of the benefits obtained with the proposed approach.