A Multiscale Antidiffusion and Restoration Approach for Gaussian Blurred Images

概要

論文の詳細を見る
Antidiffusion is a process running the diffusion equation reversely in the time domain. Though extremely important for image restoration of the Gaussian blur, it is a horribly illposed problem, since minor noise leads to very erroneous results. To solve this ill-posed problem stably, in this paper we first apply a multiscale method to decompose images into various scale components using the Gaussian and Laplacian of Gaussian(LOG)filters. We then show that the restored images can be reconstructed from the components using shrunk Gaussian and LOG filters. Our algorithm has a closed from solution, and is robust to noise because it is performed by the integration computation(convolution), contrasting with the differential computation required by direct discretization of the antidiffusion equation. The antidiffusion algorithm is also computationally efficient since the convolution is row and column separable. Finally, a comparison between the algorithm and the well-known Wiener filter is conducted. Experiments show that our algorithm is really stable and images can be restored satisfactorily.
一般社団法人電子情報通信学会の論文
1998-05-25