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We address the problem of super-resolution-obtaining high-resolution images and videos from multiple low-resolution inputs. The increased resolution can be in spatial or temporal dimensions, or even in both. We present a unified framework which uses a generative model of the imaging process and can address spatial super-resolution, space-time super-resolution, image deconvolution, single-image expansion,...
In this study, an image in painting approach using structure-guided priority belief propagation (BP) and label transformations is proposed. The proposed approach contains five stages, namely, Markov random field (MRF) node determination, structure map generation, label set enlargement by label transformations, image in painting by priority-BP optimization, and overlapped region composition. Based...
In this paper, we propose an optimized inpainting-based macroblock prediction mode in the state-of-the-art H.264/AVC video engine. Parallel with the existing intra- and inter- modes, it is regularized by the global tempo-spatial consistency between the current MB and the co-located decoded region. The target is formulated as a local optimization problem by minimizing the energy of Markov Random Field...
In this paper we propose a novel technique to image completion that addresses image renaissance through a graph-based matching process. To this end, a number of candidate seeds with content similar to the one of the area to be inpainted are considered. They are selected through a particle filter method and then positioned over the missing area. Markov random fields are used to formalize inpainting...
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