BHARDWAJ, RISHAV2024-05-232024-05-232024-05-232024-05-22http://hdl.handle.net/10012/20588Purpose: Diagnosing medical images necessitates years of experience to ensure accurate diagnoses. However, the current workforce available for this task falls significantly short compared to the volume of images requiring assessment. This places a considerable burden on the medical system during diagnosis. Additionally, medical images often contain artifacts, further complicating and prolonging the diagnostic process. This thesis serves as a solution to expedite diagnosis by enhancing the image quality of Optical Coherence Tomography Angiography (OCTA) images, thereby alleviating the strain on the system. Aims: 1. Method 1 (Chapter 2): Removal of motion artifacts from OCTA images. It is one of the toughest artifacts to be removed from an image. 2. Method 2 (Chapter 3): Super-Resolution of OCTA image. Increasing the dimensions of the image and enhancing the quality to make diagnosis process efficient. Conclusion: This work allows the removal of motion artifacts from the OCTA image and then enhance the quality of the image using super-resolution. In chapter 4 we show that the scatterplots were used to compare the correlations of the most commonly used parameters, Foveal Avascular Zone (FAZ) area, perimeter, and circularity index, between before and after super-resolution at ×2 and ×3 magnification. A p-value < 0.05 was considered significant for all statistical tests. Thus, making the diagnosis process simpler and better for medical practitioners.enOCTAsuper-resolutioncomputer visionAIMaster Thesis