A Fast Algorithm for Image Super-Resolution from Blurred Observations
- PDF / 2,508,884 Bytes
- 14 Pages / 600.03 x 792 pts Page_size
- 30 Downloads / 170 Views
A Fast Algorithm for Image Super-Resolution from Blurred Observations Nirmal K. Bose,1 Michael K. Ng,2 and Andy C. Yau3 1 Spatial
and Temporal Signal Processing Center, Department of Electrical Engineering, The Pennsylvania State University, University Park, PA 16802, USA 2 Department of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong 3 Department of Mathematics, Faculty of Science, The University of Hong Kong, Pokfulam Road, Hong Kong, China Received 1 December 2004; Revised 17 March 2005; Accepted 7 April 2005 We study the problem of reconstruction of a high-resolution image from several blurred low-resolution image frames. The image frames consist of blurred, decimated, and noisy versions of a high-resolution image. The high-resolution image is modeled as a Markov random field (MRF), and a maximum a posteriori (MAP) estimation technique is used for the restoration. We show that with the periodic boundary condition, a high-resolution image can be restored efficiently by using fast Fourier transforms. We also apply the preconditioned conjugate gradient method to restore high-resolution images in the aperiodic boundary condition. Computer simulations are given to illustrate the effectiveness of the proposed approach. Copyright © 2006 Hindawi Publishing Corporation. All rights reserved.
1.
INTRODUCTION
Image sequence super-resolution refers to methods that increase spatial resolution by fusing information from a sequence of images (with partial overlap in successive elements or frames in, e.g., video), acquired in one or more of several possible ways. For brevity, in this context, either the term super-resolution or high resolution is used to refer to any algorithm which produces an increase in resolution from multiple low-resolution degraded images. At least, two nonidentical images are required to construct a higher-resolution version. The low-resolution frames may be displaced with respect to a reference frame (Landsat images, where there is a considerable distance between camera and scene), blurred (due to causes like optical aberration, relative motion between camera and object, atmospheric turbulence), rotated and scaled (due to video camera motion like zooming, panning, tilting), and, furthermore, those could be degraded by various types of noise like signal-independent or signal-dependent, multiplicative or additive. Due to hardware cost, size, and fabrication complexity limitations, imaging systems like charge-coupled device (CCD) detector arrays often provide only multiple lowresolution degraded images. However, a high-resolution image is indispensable in applications including health diagnosis and monitoring, military surveillance, and terrain mapping by remote sensing. Other intriguing possibilities in-
clude substituting expensive high-resolution instruments like scanning electron microscopes by their cruder, cheaper counterparts and then applying technical methods for increasing the resolution to that derivable with much more costly equipment. Resolution improvement by applying tools
Data Loading...
