Abstract: A computer-implemented method of processing at least one image of a retina of an eye acquired by an ophthalmic imaging device, wherein the at least one image shows a texture of the retina. The method comprises processing a first image of the at least one image using a noise reduction algorithm based on machine learning to generate a de-noised image of the retina, wherein the texture of the retina shown in the first image is at least partially removed by the noise reduction algorithm to generate the de-noised image. The method further comprises combining a second image of the at least one image with the de-noised image to generate at least one hybrid image of the retina which shows more of the texture of the retina than the de-noised image.

Abstract: A method of method of processing optical coherence tomography, OCT, image data representing an OCT image of a retina of an eye, to generate mapping data which maps out a predetermined band of a plurality of distinct bands which extend across the OCT image and correspond to respective anatomical layers of the retina. The method comprises: receiving the OCT image data; processing A-scan data of the received OCT image data to generate data indicative of sequences of A-scan elements corresponding to the predetermined band of the plurality of distinct bands and having respective A-scan element values that vary in accordance with a predetermined pattern; and generating the mapping data by applying a line-finding algorithm to determine a line passing through the sequences of A-scan elements indicated by the generated data.