Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Rosen, Park, and Glick proposed the structured total least norm (STLN) algorithm for solving problems in which both the matrix and the right-hand side contain errors.  extend this algorithm for ill-posed problems by adding regularization, and  use the resulting algorithm to solve blind deconvolution problems as encountered in image deblurring when both the image and the blurring function have uncertainty. The resulting regularized structured total least norm (RSTLN) algorithm preserves any affine structure of the matrix and minimizes a discrete $\ell_p$-norm measure of the error, where p=1, 2, or $\infty$.  demonstrate the effectiveness of these algorithms for blind deconvolution.
['Armin Pruessner', "Dianne P. O'Leary"]
I am writing a research paper on a idea Blind Deconvolution Using a Regularized Structured Total Least Norm Algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Blind Deconvolution Using a Regularized Structured Total Least Norm Algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Rosen, Park, and Glick proposed the structured total least norm (STLN) algorithm for solving problems in which both the matrix and the right-hand side contain errors.  extend this algorithm for ill-posed problems by adding regularization, and  use the resulting algorithm to solve blind deconvolution problems as encountered in image deblurring when both the image and the blurring function have uncertainty. The resulting regularized structured total least norm (RSTLN) algorithm preserves any affine structure of the matrix and minimizes a discrete $\\ell_p$-norm measure of the error, where p=1, 2, or $\\infty$.  demonstrate the effectiveness of these algorithms for blind deconvolution."}]