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Various applications like mosaicing and object insertion require stitching of image parts and to define and get the best possible stitching, several formal cost functions are introduced for the evaluation of the stitching quality. Their performance is compared to existing methods in generation of panoramic images, object blending and removal of compression artifacts, which shows the benefits of optimizing the measures in the gradient domain.

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. Index Terms--Sensors, jitter camera, jitter video, super-resolution, motion blur.

We introduce a new kind of mosaicing, where the position of the sampling strip varies as a function of the input camera location. The new images that are generated this way correspond to a new projection model defined by two slits, termed here the Crossed-Slits (X-Slits) projection. In this projection model, every 3D point is projected by a ray defined as the line that passes through that point and intersects the two slits. The intersection of the projection rays with the imaging surface defines the image. X-Slits mosaicing provides two benefits. First, the generated mosaics are closer to perspective images than traditional pushbroom mosaics. Second, by simple manipulations of the strip sampling function, we can change the location of one of the virtual slits, providing a virtual walkthrough of a X-slits camera; all this can be done without recovering any 3D geometry and without calibration. A number of examples where we translate the virtual camera and change its orientation are given; the examples demonstrate realistic changes in parallax, reflections, and occlusions. Index Terms--Nonstationary mosaicing, crossed-slits projection, pushbroom camera, virtual walkthrough, image-based rendering.

Image mosaicing is commonly used to increase the visual field of view by pasting together many images or video frames. Existing mosaicing methods are based on projecting all images onto a predetermined single manifold: A plane is commonly used for a camera translating sideways, a cylinder is used for a panning camera, and a sphere is used for a camera which is both panning and tilting. While different mosaicing methods should therefore be used for different types of camera motion, more general types of camera motion, such as forward motion, are practically impossible for traditional mosaicing. A new methodology to allow image mosaicing in more general cases of camera motion is presented. Mosaicing is performed by projecting thin strips from the images onto manifolds which are adapted to the camera motion. While the limitations of existing mosaicing techniques are a result of using predetermined manifolds, the use of more general manifolds overcomes these limitations. Index Terms--Mosaicing, motion analysis, image alignment.