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Abstract: Multi-frame image super-resolution (SR) aims to utilize information from a set of low-resolution (LR) images to compose a high-resolution (HR) one. As it is desirable or essential in many real applications, recent years have witnessed the growing interest in the problem of multi-frame SR reconstruction. This set of algorithms commonly utilizes a linear observation model to construct the relationship between the recorded LR images to the unknown reconstructed HR image estimates. Recently, regularization-based schemes have been demonstrated to be effective because SR reconstruction is actually an ill-posed problem. Working within this promising framework, this paper first proposes two new regularization items, termed as locally adaptive bilateral total variation and consistency of gradients, to keep edges and flat regions, which are implicitly described in LR images, sharp and smooth, respectively. Thereafter, the combination of the proposed regularization items is superior to existing regularization items because it considers both edges and flat regions while existing ones consider only edges. Thorough experimental results show the effectiveness of the new algorithm for SR reconstruction. [Copyright &y& Elsevier]

Multi-Video Summarization (MVS) aims at condensing a large number of web videos by the same search query into a compact storyboard or video skimming. Although video summarization has achieved great progress, most existing work focuses only on extracting a summary from a single video. The genre-specific assumptions for Single Video Summarization (SVS) often do not hold for MVS. Moreover, the complex data characteristics among web videos make MVS more challenging. To well address the challenges in MVS, this paper presents a novel framework with Hypergraph Dominant Set (HDS), termed MVS-HDS. Specifically, we first formulate the MVS task into a problem of finding dominant sets in a hypergraph. Then, with the help of web images searched by the same query, a Query Dependent Maximum Marginal Relevance (QD-MMR) method is developed to refine these keyframe candidates by balancing the criteria of conciseness and query adaptation. Finally, to make the summary easy to understand, we further present a Graph-based Topical Closeness (GTC) method to make it more comprehensible. Extensive experiments show that the proposed framework clearly outperforms the state-of-the-art methods. [ABSTRACT FROM AUTHOR]