Super-resolution Image Reconstruction - Methods and Lessons Learned

Although there is some variation in the interpretation of the term "super-resolution" in different imaging application contexts, for computational methods it typically refers to the use of multiple images acquired at a low spatial resolution to compute a single image with increased spatial resolution. The motivation for this may be to improve the perceptual quality of the image content or to derive more accurate information from the image content such as the location of features. This may be attractive in situations where a higher resolution camera can not be used because of size or cost for example. A potential application, which may be fixed or mobile, is monitoring and surveillance. The additional information used to improve the spatial resolution may be some combination of a-priori assumptions and multiple passively acquired images in which the desired high frequency information is present, but aliased. Performance measures of super-resolution algorithms may be based on measures of image accuracy, measures of image quality, computational efficiency, or robustness in the presence of measurement noise and image acquisition model error. While computational efficiency is relatively unambiguous, the metrics for accuracy and robustness may be debated. This talk will provide an introduction to super-resolution methods and applications, explore the effects of noise and model error on resolution improvement, describe one specific project application, and discuss general lessons learned.

Speaker: Sally Wood, Santa Clara Univ.