アイテム詳細

要旨

Super resolution is the task of reconstructing one or several high resolution images, from one or several low resolution images. A variety of super resolution methods have been proposed over the past three decades, some following a singleframe based methodology while the others utilizing a multiple-frame based one. These methods are usually very sensitive to their underlying model of data and noise, which limits their performance. In this thesis, we propose and compare two multiple-frame based approaches that address such shortcomings. In the rst proposal we investigate a fast, local approach which combines the low resolution frames via warping and then performs diusion-based inpainting. The second proposal models the image formation process in a variational framework with regularization that is robust to errors in motion and blur estimation. In addition, we introduce a brightness adaptation step which results in images with sharper edges. An accurate estimation of optical ow among the low resolution measurements is a fundamental step towards high quality super resolution for both methods. Experiments conrm the eectiveness of our method on a variety of super resolution benchmark sequences, as well as its superiority in performance to other closely-related methods.