A radio astronomy image restoration algorithm based on compressed sensing framework
Deconvolution of radio astronomy images is a key data processing technique.Its main goal is to remove the effects introduced by the instrument from the observed sky images to recover the original sky images.However,radio interfer-ometer arrays employ sparse interferometric arrays,whose imaging principles differ from those of optical telescopes.If the sampling points in the UV space are not sufficiently dense,this will lead to insufficient high-resolution information during image reconstruction.Traditional radio astronomy image reconstruction algorithms fail to fundamentally solve the problem of UV space undersampling.This paper adopts the compressed sensing theoretical framework,combines prior knowledge of the sparsity of radio astronomy images,and studies a new radio astronomy image deconvolution algorithm,namely the IUWT-CS-based radio interferometric image reconstruction algorithm.This algorithm transforms the dirty im-age deconvolution process into a convex optimization problem to find the global minimum.To evaluate the reconstruction performance of this algorithm,we used the OSKAR radio astronomy simulation software package to simulate low SKA1 observations and performed deconvolution processing on the extended radio sources obtained.Experimental results show that,compared with the HOGBOM-CLEAN,MS-CLEAN,and IUWT-FISTA methods,the IUWT-CS method significantly improves the reconstruction quality of radio images and achieves finer denoising and restoration effects.
万方数据
维普
