An OmniStereo panorama consists of a pair of panoramic images, where one panorama is for the left eye, and another panorama is for the right eye. The panoramic stereo pair provides a stereo sensation up to a full 360 degrees. Omnistereo panoramas cannot be photographed by two omnidirectional cameras from two viewpoints, but can be constructed by mosaicing together images from a rotating stereo pair.A more convenient approach to generate omnistereo panoramas is by mosaicing images from a single rotating camera. This approach also enables to control stereo disparity, giving a larger baselines for faraway scenes, and a smaller baseline for closer scenes. 1Capturing panoramic omnistereo images with a rotating camera makes it impossible to capture dynamic scenes at video rates, and limits omnistereo imaging to stationary scenes. We therefore present two possibilities for capturing omnistereo panoramas using optics, without any moving parts. A special mirror is introduced such that viewing the scene through this mirror creates the same rays as those used with the rotating cameras. A lens for omnistereo panorama is also introduced. The designs of the mirror and of the lens are based on curves whose caustic is a circle.Omnistereo panoramas can also be rendered by computer graphics methods to represent virtual environments.

No abstract

No abstract

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.

Motion blur due to camera motion can significantly degrade the quality of an image. Since the path of the camera motion can be arbitrary, deblurring of motion blurred images is a hard problem. Previous methods to deal with this problem have included blind restoration of motion blurred images, optical correction using stabilized lenses, and special cmos sensors that limit the exposure time in the presence of motion. In this paper, we exploit the fundamental trade off between spatial resolution and temporal resolution to construct a hybrid camera that can measure its own motion during image integration. The acquired motion information is used to compute a point spread function (psf) that represents the path of the camera during integration. This psf is then used to deblur the image. To verify the feasibility of hybrid imaging for motion deblurring, we have implemented a prototype hybrid camera. This prototype system was evaluated in different indoor and outdoor scenes using long exposures and complex camera motion paths. The results show that, with minimal resources, hybrid imaging outperforms previous approaches to the motion blur problem. We conclude with a brief discussion on how our ideas can be extended beyond the case of global camera motion to the case where individual objects in the scene move with different velocities.