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Proceedings Paper

The ultra-rate spatial enhancement using Huber regularization MSRR and Huber high-spectrum expectation
Author(s): Vorapoj Patanavijit
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Paper Abstract

The high spatial resolution images are ultimately demanded due to the requirement of the advance digital signal processing (DSP) and digital image processing (DIP) in modern implementations thereby the image spatial enhancements, especially for an ultra-rate spatial enhanced rate, have been ultimately investigated in the DSP and DIP society in the last twenty five years. The ultra-rate spatial enhancement employed by MSRR with Huber ML (Maximum Likelihood) regularization technique and SSRR with Huber high-spectrum expectation is proposed for enhancing upto 16x spatial rate in this paper. Initially, the collection of low spatial resolution images with noise is processed by MSRR for attenuating the noise and enhancing the spatial resolution. Later, the enhanced image is processed by SSRR for calculating the high-spectrum information in order to reconstruct the extortionate spatial enhancement with 16x spatial enhanced rate. In the performance evaluation section, the simulated consequences of the proposed ultra-rate spatial enhancement are compared with other previous state-of-art (such as a bicubic interpolation technique, a classical MSRR and a classical SSRR) in both PSNR (Peak Signal to Noise Ratio) and virtual quality attitude. From the performance evaluation consequence of four noise types at many noise powers, the proposed ultra-rate spatial enhancement has a superior performance than other previous state-of-art.

Paper Details

Date Published: 8 February 2017
PDF: 6 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102251G (8 February 2017); doi: 10.1117/12.2266812
Show Author Affiliations
Vorapoj Patanavijit, Assumption Univ. (Thailand)

Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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