The research program proposed in this application aims to analyze Optical Coherence Tomography (OCT) images using sparse representations in suitable dictionaries. OCT is a new imaging technique to provide information about cross-sectional structures of an object. The approach of OCT can be thought of similarly as ultrasound imaging, but the sound profiles are replaced by coherent light beams. One of the most significant applications of OCT in clinical application is in the field of ophthalmology and has become an important tool to obtain detailed images from within the retina. An important factor that limits the effective image resolution is the occurring speckle pattern. It has two components, the signal-carrying speckle and the signal degrading speckle. The resulting effect in the OCT images is a grainy structure which blurs structural details. In order to improve the image quality, we want on the one hand suppress the speckle noise, and on the other hand may take advantage of the speckle statistics in order to improve the small image structures. This approach however bears the risk of misinterpretation, in retinal images speckles can mimic small structural features like capillaries. Therefore, special methods for noise reduction need to be developed for OCT images that take into account the a priori knowledge on the occurring speckle noise. These noise reduction techniques have then to be combined with OCT classification, segmentation and restoration methods.