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Fast minimization methods for solving constrained total-variation superresolution image reconstruction

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Abstract

In this paper, we study the problem of reconstructing a high-resolution image from several decimated, blurred and noisy low-resolution versions of the high-resolution image. The problem can be formulated as a combination of the total variation (TV) inpainting model and the superresolution image reconstruction model. The main purpose of this paper is to develop an inexact alternating direction method for solving such constrained TV image reconstruction problem. Experimental results are given to show that the proposed algorithm is effective and efficient.

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Authors and Affiliations

  1. Centre for Mathematical Imaging and Vision and Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Michael Ng & Fan Wang

  2. Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong

    Xiao-Ming Yuan

Authors
  1. Michael Ng
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  2. Fan Wang
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  3. Xiao-Ming Yuan
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Correspondence to Michael Ng.

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Ng, M., Wang, F. & Yuan, XM. Fast minimization methods for solving constrained total-variation superresolution image reconstruction. Multidim Syst Sign Process 22, 259–286 (2011). https://doi.org/10.1007/s11045-010-0137-9

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  • DOI: https://doi.org/10.1007/s11045-010-0137-9

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