Super-resolution with the application on ear imagesMarkićević, Luka (Avtor) Peer, Peter (Mentor) Štruc, Vitomir (Komentor) Emeršič, Žiga (Komentor) super-resolutionearsEDSRSwinIRPSNRSSIMSuper-resolution (SR) is a class of image enhancing methods that boosts the resolution of the image. This is useful in various areas, such as visually enhancing photographs or improving person recognition performance. This undergraduate thesis focuses on Single Image Super Resolution of ears, a method of super-resolution that creates missing information from a single image. One of the earliest ways to address the issue of super-resolution was interpolation, but achieved limited success. The latest improvements in SR that have been made feasible by deep neural networks, which significantly improved performance. We evaluated the performance of the Enhanced Deep Residual Network (EDSR) and Shifted Windows Transformer Network (SwinIR) for image super-resolution of ears. Using the AWE dataset which consists of $16,665$ images of ears of various sizes, shapes, and orientations, we trained four models: two on EDSR and two on SwinIR networks, each with scaling factor of two and four. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) performance measures were used to evaluate the two different model designs. SwinIR achieves a superior PSNR and SSIM, however, the visual results seem to be highly similar.20222022-09-14 19:20:00Diplomsko delo/naloga140426VisID: 34944COBISS_ID: 123535619sl