Issue No. 06 - June (2005 vol. 27)
M. Ben-Ezra , Siemens Corp. Res. Inc., Princeton, NJ, USA
Video cameras must produce images at a reasonable frame-rate and with a reasonable depth of field. These requirements impose fundamental physical limits on the spatial resolution of the image detector. As a result, current cameras produce videos with a very low resolution. The resolution of videos can be computationally enhanced by moving the camera and applying super-resolution reconstruction algorithms. However, a moving camera introduces motion blur, which limits super-resolution quality. We analyze this effect and derive a theoretical result showing that motion blur has a substantial degrading effect on the performance of super-resolution. The conclusion is that, in order to achieve the highest resolution motion blur should be avoided. Motion blur can be minimized by sampling the space-time volume of the video in a specific manner. We have developed a novel camera, called the "jitter camera," that achieves this sampling. By applying an adaptive super-resolution algorithm to the video produced by the jitter camera, we show that resolution can be notably enhanced for stationary or slowly moving objects, while it is improved slightly or left unchanged for objects with fast and complex motions. The end result is a video that has a significantly higher resolution than the captured one.
Detectors, Spatial resolution, Cameras, Sampling methods, Jitter, Reconstruction algorithms, Image motion analysis, Performance analysis, Motion analysis, Degradation
M. Ben-Ezra, A. Zomet and S. Nayar, "Video super-resolution using controlled subpixel detector shifts," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 27, no. 6, pp. 977-987, .