The recent increase in the wide 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 images. The demand for such high-resolution (HR) images can be met by algorithmic advances in super-resolution (SR) technology in place ofXor in tandem with 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 approach to obtaining 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 (super-resolution) 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 high-quality imaging is the topic of this international conference. The scope of techniques intended to overcome the above limitations that will be covered in this international conference 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.

Conference Organizers:

Michael Ng (The University of Hong Kong)
Raymond Chan (The Chinese University of Hong Kong)
S.C. Chan (The University of Hong Kong)
Wai-Ki Ching (The University of Hong Kong)
Edmund Lam (The University of Hong Kong)
Chong-Sze Tong (Hong Kong Baptist University)