摘要
In this paper we solve large scale ill-posed problems, particularly the image restoration problem in atmospheric imaging sciences, by a trust region-CG algorithm. Image restoration involves the removal or minimization of degradation (blur, clutter, noise, etc.) in an image using a priori knowledge about the degradation phenomena. Our basic technique is the so-called trust region method, while the subproblem is solved by the truncated conjugate gradient method, which has been well developed for well-posed problems.The trust region method, due to its robustness in global convergence, seems to be a promising way to deal with ill-posed problems.
In this paper we solve large scale ill-posed problems, particularly the image restoration problem in atmospheric imaging sciences, by a trust region-CG algorithm. Image restoration involves the removal or minimization of degradation (blur, clutter, noise, etc.) in an image using a priori knowledge about the degradation phenomena. Our basic technique is the so-called trust region method, while the subproblem is solved by the truncated conjugate gradient method, which has been well developed for well-posed problems. The trust region method, due to its robustness in global convergence, seems to be a promising way to deal with ill-posed problems.
基金
This work was partially supported by the National Natural Science Foundation of China (Grant No. 19731010)
the Knowledge Innovation Program of CAS.
