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Article dans une revue

A new low-complexity patch-based image super-resolution

Abstract : In this study, a novel single image super-resolution (SR) method, which uses a generated dictionary from pairs of high-resolution (HR) images and their corresponding low-resolution (LR) representations, is proposed. First, HR and LR dictionaries are created by dividing HR and LR images into patches Afterwards, when performing SR, the distance between every patch of the input LR image and those of available LR patches in the LR dictionary are calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary will be passed through an illumination enhancement process resulting in consistency of illumination between neighbour patches. This process is applied to all patches of the LR image. Finally, in order to remove the blocking effect caused by merging the patches, an average of the obtained HR image and the interpolated image is calculated. Furthermore, it is shown that the stabe of dictionaries is reducible to a great degree. The speed of the system is improved by 62.5%. The quantitative and qualitative analyses of the experimental results show the superiority of the proposed technique over the conventional and state-of-the-art methods.
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https://hal.univ-angers.fr/hal-02528726
Contributeur : Okina Université d'Angers <>
Soumis le : mercredi 1 avril 2020 - 23:40:29
Dernière modification le : jeudi 2 avril 2020 - 10:26:05

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Pejman Rasti, Kamal Nasrollahi, Olga Orlova, Gert Tamberg, Thomas Moeslund. A new low-complexity patch-based image super-resolution. IET Computer Vision, IET, 2017, 11 (7), pp.567-576. ⟨10.1049/iet-cvi.2016.0463⟩. ⟨hal-02528726⟩

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