Algorithms for super-resolution of images and videos based on learning methods. (Algorithmes de super-résolution d'images et de vidéos basés sur des méthodes d'apprentissage)

With super-resolution (SR) we refer to a class of techniques that enhance the spatial resolution of images and videos. SR algorithms can be of two kinds: multi-frame methods, where multiple low-resolution images are aggregated to form a unique high-resolution image, and single-image methods, that aim at upscaling a single image. This thesis focuses on developing theory and algorithms for the single-image SR problem. In particular, we adopt the so called example-based approach, where the output image is estimated with machine learning techniques, by using the information contained in a dictionary of image “examples”. The examples consist in image patches, which are either extracted from external images or derived from the input image itself. For both kinds of dictionary, we design novel SR algorithms, with new upscaling and dictionary construction procedures, and compare them to state-of-the-art methods. The results achieved are shown to be very competitive both in terms of visual quality of the super-resolved images and computational complexity. We then apply our designed algorithms to the video upscaling case, where the goal is to enlarge the resolution of an entire video sequence. The algorithms, opportunely adapted to deal with this case, are also analyzed in the coding context. The analysis conducted shows that, in specific cases, SR can also be an effective tool for video compression, thus opening new interesting perspectives.