Super-Resolution Reconstruction and Its Future Research Direction
Vorapoj Patanavijit · 2009
During this decade, the enlargement in the extensive 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 SuperResolution Reconstruction (SRR) technology in addition to hardware development. Not only do such HR images give the viewer a more pleasing picture but also offer additional details that are significant for subsequent analysis in many applications. SRR algorithm is considered to be one of the most promising techniques that can help overcome the limitations due to optics and sensor resolution. In general, the problem of super-resolution can be expressed as that of combining a set of aliased, noisy, lowresolution, blurry images to produce a higher resolution image or image sequence. The idea is to increase the information content in the final image by exploiting the additional spatio-temporal information that is available in each of the LR images. Consequently, the major advantage of the SRR algorithm is that it may cost less and the existing LR imaging systems can be still utilized. The SRR algorithm is proved to be useful in several practical cases where multiple frames of the same scene can be obtained, including medical imaging, satellite imaging, and video applications.