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Enhancement of Infrared Images Using Super Resolution Techniques Based on Big Data Processing |
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| Authors: | Abd El-Samie Fathi E Ashiba Huda I Shendy H Mansour Hala M Ahmed Hossameldin M Taha Taha E Dessouky Moawad I Elkordy Mohamed F Abd?Elnaby Mohammed El-Fishawy Adel S |
| Affiliation: | 1.Department of Electronics and Electrical Communications Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt ;2.Department of Electronics and Electrical Communications, Bilbis Higher Institute of Engineering, Bilbis, Sharqia, Egypt ;3.Department of Electronics and Electrical Communications, Shoubra Faculty of Engineering, Benha University, Benha, Egypt ;4.Department of Computer Engineering, College of Computers and Information Technology, Taif University, Al?Hawiya, 21974, Saudi Arabia ; |
| 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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