A high-resolution point-spread function deconvolution imaging method based on Gaussian smoothing X-shaped denoising diffusion filtering operator

Most of the migration imaging methods use iterative gradient update methods to approximate the inverse of the Hessian matrix, but such approximations are not very accurate and consume a lot of computational time for iterations. In order to calculate the inverse of the Hessian matrix more accurately and eliminate the noise in migration imaging process, we propose a high-resolution point-spread function (PSF) deconvolution imaging method based on Gaussian smoothing X-shaped denoising diffusion filtering operator. In optics, the image of a complex object is considered to be a degenerate result obtained by convolving the PSF with the real object. Extracting a row of the Hessian matrix to image is like the spot formed by the PSF, so we can use the PSF deconvolution to compute the inverse of the Hessian matrix. In addition, the Gaussian smoothing X-shaped denoising diffusion filtering operator eliminates noise introduced by PSF deconvolution. We compare our method with the Laplacian operator, the X-shaped denoising diffusion filtering operator, and the Gaussian smoothing Laplacian operator using the Marmousi model. The results show that our method produces more accurate image with higher SNR and higher resolution than other methods.