THz Super-Resolution Imaging Based on Complex Laplacian Prior Deconvolution Algorithm
Ying Wang, Feng Qi, Jinkuan Wang · 2021
Due to the long wavelength of the Terahertz (THz) wave, the imaging quality is seriously deteriorated with diffraction. To solve this problem, a simple but very effective approach based on prior knowledge and wave nature was introduced in this paper. In this prior, the image gradients are represented by Laplacian to constrain the gradient of the high-resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. Moreover, the deconvolution algorithm is expended to a complex dimension. Low-Resolution (LR) THz image was simulated by convolution the High-Resolution (HR) image with real-measured Point-Spread Function (PSF) to ensure the applicability. The numerical experiments illustrate the efficiency and effectiveness of the proposed method in terms of Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE) and Structural Similarity (SSIM). Super-Resolution (SR) results show that the proposed method has good performance in convergence and suppressing ringing or jaggy artifacts.