Abstract
This paper presents a super-resolution (SR) technique for enhancement of infrared (IR) images. The suggested technique relies on the image acquisition model, which benefits from the sparse representations of low-resolution (LR) and high-resolution (HR) patches of the IR images. It uses bicubic interpolation and minimum mean square error (MMSE) estimation in the prediction of the HR image with a scheme that can be interpreted as a feed-forward neural network. The suggested algorithm to overcome the problem of having only LR images due to hardware limitations is represented with a big data processing model. The performance of the suggested technique is compared with that of the standard regularized image interpolation technique as well as an adaptive block-by-block least-squares (LS) interpolation technique from the peak signal-to-noise ratio (PSNR) perspective. Numerical results reveal the superiority of the proposed SR technique.






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Abd El-Samie, F.E., Ashiba, H.I., Shendy, H. et al. Enhancement of Infrared Images Using Super Resolution Techniques Based on Big Data Processing. Multimed Tools Appl 79, 5671–5692 (2020). https://doi.org/10.1007/s11042-019-7634-0
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DOI: https://doi.org/10.1007/s11042-019-7634-0