The blurring that occurs in the lens of a camera has a tendency to further degrade in areas away from the on-axis of the
image. In addition, the degradation of the blurred image in an off-axis area exhibits directional dependence.
Conventional methods have been known to use the Wiener filter or the Richardson–Lucy algorithm to mitigate the
problem. These methods use the pre-defined point spread function (PSF) in the restoration process, thereby preventing an
increase in the noise elements. However, the nonuniform degradation that depends on the direction is not improved even
though the edges are emphasized by these conventional methods. In this paper, we analyze the directional dependence of
resolution based on the modeling of an optical system using a blurred image. We propose a novel image deblurring
method that employs a reverse filter based on optimizing the directional dependence coefficients of the regularization
term in the maximum a posterior probability (MAP) algorithm. We have improved the directional dependence of
resolution by optimizing the weight coefficients of the direction in which the resolution is degraded.