Joint Deblurring and Denoising of THz Time-Domain Images

Terahertz (THz) pulse/time-domain imaging attracted increased interest in recent years mostly due to its ability to extract dielectric properties of sample materials (i.e., absorption coefficient and the refraction index) from the amplitude and phase of each spectral component of the THz pulse. The resulting data from a THz time-domain system represents a 3-dimensional (3D) hyperspectral cube which contains several 2D images corresponding to different frequencies or bands. Due to a frequency-dependent non-zero THz beam waist, these 2D images are corrupted by blurring artifacts: a THz beam waist is wider on lower frequencies leading to more blurry corresponding 2D images. At higher frequencies, the beam waist is smaller resulting in sharper, but noisier images due to the decrease in the THz signal amplitude. The main focus of this work is the joint reduction of blur and noise from THz time-domain images. We propose two instances of a fast joint deblurring and denoising approach which is able to deal with THz time-domain images corrupted by different noise types and frequency-dependent blur. The experiments performed on synthetic and real THz time-domain images show that the proposed approach outperforms conventional 2D deblurring approaches and methods tailored to remote sensing hyperspectral images. To the best of our knowledge, this is the first time that a joint deblurring and denoising approach tailored to THz time-domain images is proposed taking into consideration band-dependent blur and different noise types.

Automated summary

Terahertz pulse/time-domain imaging is able to extract dielectric properties of sample materials, but the resulting data is complex and affected by frequency-dependent blur and noise. Two fast joint deblurring and denoising approaches have been proposed to improve image quality in THz time-domain images.