Compressive sensing-based tomography for Absolute X-ray UltraViolet (AXUV) diagnostics

Abstract

Absolute x-ray ultraviolet diagnostics ensures 2D coverage of the radiation emission region that enables tomographic reconstruction. However, retrieving the local emissivity via tomography remains a challenge due to its ill-posed nature. Tikhonov regularization with smoothness operation generally performs well but tends to over-smooth regions with steep gradients and local structure in the radiation profile and may introduce artifacts. In this paper, a tomography method based on compressive sensing theory with Tikhonov regularization terms is developed. Experimental results on multiple phantom sets show that the proposed method improves the reconstruction accuracy and quality in regions with steep gradients compared with the Tikhonov regularization method and suppresses the unphysical negative emissivity. The analysis of reconstruction uncertainty shows that the dictionary learning process provides more accurate prior information about steep gradients to improve the quality of reconstructed images, and compressive sensing has the denoising capability to reduce the impact of noise. Finally, the method is validated by data from the Sino-UNIted Spherical Tokamak, showing fewer artifacts and more reliable reconstruction images than the earlier method.