Retinal vessel inpainting using recursive least square dictionary learning algorithm

Retinal blood vessels are considered as being interference on the retinal images for the task of detecting significant features of the most frequent eye diseases. If these blood vessel structures could be suppressed, it might lead to a more accurate segmentation of retinal lesions as well as a better extraction of textural features to be used for pathology detection. This work proposes, as a novelty, the use of sparse representations and dictionary learning techniques for retinal vessel inpainting. The dictionary learning algorithms used in this paper were the Recursive Least Square Dictionary Learning (RLS-DL), and Online Dictionary Learning (ODL). We tested the performance of the algorithm for grayscale and RGB images from the DRIVE public database, employing different neighbourhoods and sparseness factors. An average recovery error smaller than 0.022 was achieved. The results suggest that the use of sparse representations and dictionaries learned by RLS-DLA performs very well for inpainting of retinal blood vessels.