Zvýšení kvality v obrazu obličeje s použitím sekvence snímků

Abstract

Diplomova praca sa zameriava na oblast zaostrovania obrazkov tvari. V teoretickej casti prace budu prezentovane moderne metody zaostrovania obrazkov pomocou jedineho obrazku a metody editacie obrazkov. Prakticka cast sa zameria na pristupy rekonstrukcie obrazkov zo sekvencie poskodenych obrazkov. Viacere modely neuronovych sieti so vstupom pre viacero obrazkov budu zhotovene a vyhodnotene. Alternativny pristup v podobe balika nastrojov na editaciu obrazkov bude taktiez predstaveny. Tieto nastroje budu vyuzivat najmodernejsie pristupy k editacii obrazkov s cielom spojit vizualne prvky tvari zo vstupnej sekvencie obrazkov do jedneho finalneho vystupu. V zavere prace budu vsetky metody navzajom porovnane.Master’s thesis delves into the field of face super-resolution. It aims to review novel approaches to single-frame image sharpening and image editing in the theoretical part of the work. Practical part will focus on approaches to image reconstruction from a sequence of damaged images. Multiple multi-frame neural network models will be implemented and evaluated. As alternative option, a suite of image editing tools will be presented as well. These tools will utilize most modern image editing techniques to merge visual features of faces from multiple input images into a single output image. At the end of the thesis, all methods will be compared to each other.