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High dynamic range (HDR) video synthesis is a very challenging task. Consecutive frames are acquired with alternate expositions, generally two or three different exposure times. Classical methods aim at registering neighboring frames and fuse them using image HDR techniques. However, the registration often fails to obtain accurate results and the fusion produces ghosting artifacts. Deep learning techniques have recently appeared imitating the structure of existing classical methods. The neural network is intended to estimate the registration function and choose the fusion weights. In this paper, we propose a new method for HDR video synthesis using a variational model. The proposed model uses a nonlocal regularization term to combine pixel information from neighboring frames. The obtained results are competitive with state-of-the-art. Moreover, the proposed method gives a more reliable and understandable solution than deep-learning based ones. • HDR video synthesis by using classical techniques. • We use a variational model with non-local regularization. • The proposed method obtains competitive results. • The proposed method is more reliable and understandable that deeplearning methods. [ABSTRACT FROM AUTHOR]

Multi-exposure fusion (MEF) is a technique that combines different snapshots of the same scene, captured with different exposure times, into a single image. This combination process (also known as fusion) is performed in such a way that the parts with better exposure of each input image have a stronger influence. Therefore, in the result image all areas are well exposed. In this paper, we propose a new method that performs MEF and noise removal. Rather than denoising each input image individually and then fusing the obtained results, the proposed strategy jointly performs fusion and denoising in the Discrete Cosinus Transform (DCT) domain, which leads to a very efficient algorithm. The method takes advantage of spatio-temporal patch selection and collaborative 3D thresholding. Several experiments show that the obtained results are significantly superior to the existing state of the art. [ABSTRACT FROM AUTHOR]

This paper presents an overview of the most cited papers in all the journals indexed in the Web of Science in the tourism, leisure and hospitality fields through a bibliometric approach. The paper presents several lists with the most cited papers and the publication and citation structure of the last decades in the tourism, leisure and hospitality fields. It also analyzes how the main topics and keywords have changed in these areas in order to raise awareness of the topics and keywords that will predominate in the research in these fields over the next years. [ABSTRACT FROM AUTHOR]

Abstract: We develop a new decision making approach for dealing with uncertain information and apply it in tourism management. We use a new aggregation operator that uses the uncertain weighted average (UWA) and the uncertain induced ordered weighted averaging (UIOWA) operator in the same formulation. We call it the uncertain induced ordered weighted averaging – weighted averaging (UIOWAWA) operator. We study some of the main advantages and properties of the new aggregation such as the uncertain arithmetic UIOWA (UA-UIOWA) and the uncertain arithmetic UWA (UAUWA). We study its applicability in a multi-person decision making problem concerning the selection of holiday trips. We see that depending on the particular type of UIOWAWA operator used, the results may lead to different decisions. [Copyright &y& Elsevier]