Image super-resolution (SR) plays a significant role in Internet of Vehicles (IoV), and is widely used in many important applications, such as object recognition and vehicle identification. However, in certain cases, the quality of acquired images is low and general SR algorithms are inapplicable. Aiming at the disadvantages of the traditional image super- resolution methods which are mainly based on interpolation and example learning, this paper utilizes compressed sensing (CS) and sequence information to present a new approach of image super-resolution. In order to keep high frequency information and reduce ringing and jagged artifacts, the proposed method takes advantage of the useful information between multiple image frames and jointly trains coupled dictionaries for the low- resolution (LR) and high-resolution (HR) image block pair. By fusing atoms of LR dictionary, the atom of HR dictionary is obtained and the HR image can be recovered in terms of the learned HR dictionary. The experimental results show that the proposed algorithm has better performance in both subjective assessment and objective standards, including Entropy and Average Gradient.


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    Titel :

    Super-Resolution of Low-Quality Images Based on Compressed Sensing and Sequence Information


    Beteiligte:
    Zhou, Ruofei (Autor:in) / Wang, Gang (Autor:in) / Zhao, Donglai (Autor:in) / Zou, Yikun (Autor:in) / Zhang, Tong (Autor:in)


    Erscheinungsdatum :

    2019-09-01


    Format / Umfang :

    620863 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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