Abstract
In this paper, we propose to address spatial Super Resolution (SR) from multiple registered Low-Resolution (LR) observations. We formulate a scheme to estimate a High-Resolution (HR) image from weighted combination of LRs. We propose an evidence- based technique to compute confidence factor, assigned as weights to each LR observation. We generate evidence parameters using variations in intensity and distance of registered LR images and combine them using Dempster–Shafer Combination Rule (DSCR) to generate confidence factor. We show that spatial super-resolution is obtained while retaining the high-frequency information in the image and demonstrate the same. We perform quality analysis of the super- resolved image using qualitative and quantitative approaches.
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Patil, U., Tabib, R.A., Konin, C.M., Mudenagudi, U. (2018). Evidence-Based Framework for Multi-image Super-Resolution. In: Sa, P., Bakshi, S., Hatzilygeroudis, I., Sahoo, M. (eds) Recent Findings in Intelligent Computing Techniques . Advances in Intelligent Systems and Computing, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-8633-5_41
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DOI: https://doi.org/10.1007/978-981-10-8633-5_41
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