Using Markov constraint and constrained least square filter to develop a novel method of passive terahertz image restoration

Abstract

In recent years, passive terahertz imaging has gained significant attention in both research and practice. One big challenge with passive terahertz imaging is its low-quality images with high level of noise. State-of-the art image restoration methods have been developed for image denoising, such as methods based on Markov constraint and regular filter methods. Building upon these two methods, this paper develops a novel method for passive terahertz image restoration which preserves well both high frequency and low frequency information of the images. Performance of our method is evaluated using two common image criteria of the image sharpness, i.e. edge intensity and definition. Experimental results showed our method outperform state-of-the art methods for passive terahertz image restoration.