Reconstructing arbitrarily focused images from two differently focused images using linear filters

We present a novel filtering method for reconstructing an all-in-focus image or an arbitrarily focused image from two images that are focused differently. The method can arbitrarily manipulate the degree of blur of the objects using linear filters without segmentation. The filters are uniquely determined from a linear imaging model in the Fourier domain. An effective and accurate blur estimation method is developed. The simulation results show that the accuracy and computational time of the proposed method are improved compared with the previous iterative method and that the effects of blur estimation error on the quality of the reconstructed image are very small. The method performs well for real images acquired without visible artifacts.

I. Introduction

Use of multiple images acquired with different camera settings, such as position and focus, gives rise to various processing tasks in many fields of image processing and computer vision. Examples of such tasks include mutlichannel image restoration [1] [2] [3], wide-view image generation by the mosaicking method [4], arbitrary-view image generation using image-based rendering techniques [5], [6], high dynamic-range capture and rendering [7], [8], and depth estimation by stereo matching or depth-from-defocus/focus methods [9], [10].