An Adaptive Super-Resolution Reconstruction for Terahertz Image Based on MRF Model

Abstract:

A method that the adaptive super-resolution reconstruction for Terahertz (THz) image based on the Markov random field (MRF) is proposed. The adaptive Gaussian weighting factor based on the Markov prior distribution is applied to the smoothness of the image edge. The gradient-based optimization converges to the optimal solution fast. It simulates the fact Terahertz image to verify the feasibility of the method comparing with the traditional maximum a posteriori (MAP) super-resolution algorithm. The experimental results show that the adaptive Gaussian weighting super-resolution algorithm not only has high super-resolution performance, but also can better maintain the image edge information and reduce the noise of restored images, and get an ideal THz image. An adaptive super-resolution reconstruction method can be used for Terahertz image reconstruction.

• Periodical: Applied Mechanics and Materials (Volumes 373-375)
• Pages: 541-546
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.373-375.541
• Online since: August 2013
• Authors: Yun Yu Xie*, Chang Hua Hu, Biao Shi, Qian Man
• Keywords: Adaptive Gaussian Weighting, Markov Random Field, Super-Resolution Reconstruction Algorithm, Terahertz Image

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