Super-Resolution Imaging: Analysis, Algorithms, and Applications
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Editorial Super-Resolution Imaging: Analysis, Algorithms, and Applications Michael Ng,1 Tony Chan,2 Moon Gi Kang,3 and Peyman Milanfar4 1 Department
of Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong of Mathematics, University of California, Los Angeles, CA 90095-1555, USA 3 Department of Electrical and Electronic Engineering, Yonsei University, Seoul 120-749, Korea 4 Department of Electrical Engineering, University of California, Santa Cruz, CA 95064, USA 2 Department
Received 2 August 2005; Accepted 2 August 2005 Copyright © 2006 Michael Ng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The recent increase in the widespread use of digital imaging technologies in consumer (e.g., digital video) and other markets (e.g., security and military) has brought with it a simultaneous demand for higher-resolution (HR) images. The demand for such images can be partially met by algorithmic advances in super-resolution (SR) technology in addition to hardware development. Such HR images not only give the viewer a more pleasing picture but also offer additional details that are important for subsequent analysis in many applications. The current hardware approach to obtain HR images mainly relies on sensor manufacturing technology that attempts to increase the number of pixels per unit area by reducing the pixel size. However, the cost for high-precision optics and sensors may be prohibitive for general purpose commercial applications, and there is a limitation to pixel size reduction due to shot noise encountered in the sensor itself. Therefore, a resolution enhancement (SR) approach using computational, mathematical, and statistical techniques has received a great deal of attention recently. The relevant signal processing technology for this SR approach to highquality imaging is the topic of this special issue. The scope of techniques intended to overcome the above limitations that will be covered in this special issue will include enhancement in spatial resolution for both gray-scale and color images and video, suppression of signal-dependent noise, and various other associated artifacts. Because of the recent emergence of many key-relevant computational, mathematical, and statistical techniques, and the increasing importance of digital imaging technology, a special issue of the EURASIP JASP dedicated to the topic of SR imaging is quite timely.
This special issue contains sixteen articles. The first seven articles by M. Vega et al., M.-C. Pan, S. Farsiu et al., G. M. Callico et al., B.-W. Jeon et al., N. K. Bose et al., and T. Q. Pham et al. are on the computational, mathematical and statistical techniques for SR imaging. The next three articles by P. Vandewalle et al., M. Trimeche et al., and M. Balci and H. Foroosh are on the subject of subpixel registration of lowresolution images in image reconstruction. The next four articles by C
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