OPTICAL FLOW ESTIMATION IN OCULAR ENDOSCOPY VIDEOS USING FLOWNET ON SIMULATED ENDOSCOPY DATA

In recent years, endoscopy has been increasingly used for ocular surgeries whenever viewing with a microscope is altered by occlusion or opacity. However, ocular endoscopy suffers from several limitations, including reduced field of view and limited resolution, which may compromise its usability. Hopefully, image processing techniques such as mosaicking and super-resolution could help in alleviating these problems, by artificially enlarging the field and increasing resolution. All these techniques rely on the ability to estimate the optical flow between consecutive frames, which is particularly challenging for those images. This paper investigates the use of the state-of-the-art FlowNet algorithm for motion estimation in ocular endoscopy videos. Because FlowNet is strongly supervised, an artificial dataset of consecutive image pairs with ground truth optical flow is generated using eye fundus photographs from Kaggle's Diabetic Retinopathy Detection dataset. A FlowNet model, initialized on the public Flying Chairs dataset, is fine-tuned on these images. Initial experiments show that, unlike any other optical flow estimation method, this model can successfully capture motion between ocular endoscopy image pairs.