Image restoration based on multiple PSF information with applications to phase-coded imaging system

Abstract

Conventional image restoration technique generally uses one point-spread function (PSF) corresponding to an object distance (OD) and a viewing angle (VA) in filter design. However, for those imaging systems, which concern a better balance or a new tradeoff of image restoration within a range of ODs or VAs, the conventional design might be insufficient to give satisfactory results. In this paper, an extension of the minimum mean square error (MMSE) method is proposed. The proposed method defines a cost function as a linear combination of multiple mean square errors (MSEs). Each MSE is for measuring the restoration performance at a specific OD and VA and can be computed from the restored image and its correspondent target image. Since the MSEs for different ODs are lumped into one cost function, the filter solved can provide a better balance in restoration compared with the conventional design. The method is applied to an extended depth-of-field (EDoF) imaging system and computer simulations are performed to verify its effectiveness.