Computer Vision News 36 AI for Ophthalmology by RSIP Vision In medical imaging, especially in radiology, deep learning has been used in recent years to gain super-resolution . On one hand, it is possible to scan faster and more efficiently at lower resolutions. On the other hand, quality scans provide added benefits. Super-resolution is a class of techniques in image processing that enhance the resolution of an imaging system, making images sharper and clearer . By improving the quality of OCT images, super-resolution can potentially enhance our ability to detect and analyze pathological changes , thus facilitating more accurate and early diagnosis of various eye conditions. OCT images are frequently used to demonstrate the retina and understand the different pathologies and decide the diagnostics and help with procedure planning. The figure above shows the prediction image as compared to the ground truth, where prediction was restored from an image with a resolution reduced by a factor of 2. Pay attention to the fact that we are always dealing with images in a given size. What superresolution or upsampling gives us is “more slices”, meaning the distance between slices will be lower because we have predicted the slices between them.
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