Image-fusion-based adaptive regularization for image expansion

This paper presents a regularized image sequence interpolation algorithm, which can restore high frequency details by fusing low-resolution frames. Image fusion algorithm gives the feasibility of using different data sets, which correspond to the same scene to get a better resolution and information of the scene than the one obtained using only one data set. Based on the mathematical model of image degradation, we can have an interpolated image which minimizes both residual between the high resolution and the interpolated images with a prior constraint. In addition, by using spatially adaptive regularization parameters, directional high frequency components are preserved with efficiently suppressed noise. The proposed algorithm provides a better-interpolated image by fusing low-resolution frames. We provide experimental results which are classified into non-fusion and fusion algorithms. Based on the experimental results, the proposed algorithm provides a better interpolated image than the conventional interpolation algorithms in the sense of both subjective and objective criteria. More specifically, the proposed algorithm has the advantage of preserving high frequency components and suppressing undesirable artifacts such as noise.