Indexed by:期刊论文

Date of Publication:2014-04-01

Journal:SIGNAL PROCESSING-IMAGE COMMUNICATION

Included Journals:SCIE、EI、Scopus

Volume:29

Issue:4

Page Number:514-529

ISSN No.:0923-5965

Key Words:Super-resolution; Non-local regularization; Motion estimation; Blur kernel estimation

Abstract:Video super-resolution (SR) is a process for reconstructing high-resolution (HR) images by utilizing complementary information among multiple low-resolution (LR) images. Accurate estimation of the motion among the LR images significantly affects the quality of the reconstructed HR image. In this paper, we analyze the possible reasons for the inaccuracy of motion estimation and then propose a multi-lateral filter to regularize the process of motion estimation. This filter can adaptively correct motion estimation according to the estimation reliability, image intensity discontinuity, and motion dissimilarity. Furthermore, we introduce a non-local prior to solve the ill-posed problem of HR image reconstruction. This prior can fully utilize the self-similarities existing in natural images to regularize the HR image reconstruction. Finally, we employ a Bayesian formulation to incorporate the two regularizations into one Maximum a Posteriori (MAP) estimation model, where the HR image and the motion estimation can be refined progressively in an alternative and iterative manner. In addition, an algorithm that estimates the blur kernel by analyzing edges in an image is also presented in this paper. Experimental results demonstrate that the proposed approaches are highly effective and compare favorably to state-of-the-art SR algorithms. (C) 2014 Elsevier B.V. All rights reserved.