Shmuel Peleg

Improving resolution by image registration

Abstract Image resolution can be improved when the relative displacements in image sequences are known accurately, and some knowledge of the imaging process is available. The proposed approach is similar to back-projection used in tomography. Examples of improved image resolution are given for gray-level and color images, when the unknown image displacements are computed from the image sequence.

Motion Analysis for Image Enhancement: Resolution, Occlusion, and Transparency

Abstract Accurate computation of image motion enables the enhancement of image sequences. In scenes having multiple moving objects the motion computation is performed together with object segmentation by using a unique temporal integration approach. After the motion for the different image regions is computed, these regions can be enhanced by fusing several successive frames covering the same region. Enhancements treated here include improvement of image resolution, filling-in occluded regions, and reconstruction of transparent objects.

Multiple Resolution Texture Analysis and Classification

Textures are classified based on the change in their properties with changing resolution. The area of the gray level surface is measured at serveral resolutions. This area decreases at coarser resolutions since fine details that contribute to the area disappear. Fractal properties of the picture are computed from the rate of this decrease in area, and are used for texture comparison and classification. The relation of a texture picture to its negative, and directional properties, are also discussed.

Computing occluding and transparent motions

Computing the motions of several moving objects in image sequences involves simultaneous motion analysis and segmentation. This task can become complicated when image motion changes significantly between frames, as with camera vibrations. Such vibrations make tracking in longer sequences harder, as temporal motion constancy cannot be assumed. The problem becomes even more difficult in the case of transparent motions.A method is presented for detecting and tracking occluding and transparent moving objects, which uses temporal integration without assuming motion constancy. Each new frame in the sequence is compared to a dynamic internal representation image of the tracked object. The internal representation image is constructed by temporally integrating frames after registration based on the motion computation. The temporal integration maintains sharpness of the tracked object, while blurring objects that have other motions. Comparing new frames to the internal representation image causes the motion analysis algorithm to continue tracking the same object in subsequent frames, and to improve the segmentation.

Seamless Image Stitching in the Gradient Domain

Image stitching is used to combine several individual images having some overlap into a composite image. The quality of image stitching is measured by the similarity of the stitched image to each of the input images, and by the visibility of the seam between the stitched images.

Robust super-resolution

A robust approach for super-resolution is, presented, which is especially valuable in the presence of outliers. Such outliers may be due to motion errors, inaccurate blur models, noise, moving objects, motion blur etc. This robustness is needed since super-resolution methods are very sensitive to such errors. A robust median estimator is combined in an iterative process to achieve a super resolution algorithm. This process can increase resolution even in regions with outliers, where other super resolution methods actually degrade the image.

Image sequence enhancement using sub-pixel displacements

Given a sequence of images taken from a moving camera, they are registered with subpixel accuracy in respect to translation and rotation. The subpixel registration allows image enhancement with respect to improved resolution and noise cleaning. Both the registration and the enhancement procedures are described. The methods are particularly useful for image sequences taken from an aircraft or satellite where images in a sequence differ mostly by translation and rotation. In these cases, the process results in images that are stable, clean, and sharp.<<ETX>>

Panoramic mosaics by manifold projection

As the field of view of a picture is much smaller than our own visual field of view, it is common to paste together several pictures to create a panoramic mosaic having a larger field of view. Images with a wider field of view can be generated by using fish-eye lens, or panoramic mosaics can be created by special devices which rotate around the cameras optical center (Quicktime VR, Surround Video), or by aligning, and pasting, frames in a video sequence to a single reference frame. Existing mosaicing methods have strong limitations on imaging conditions, and distortions are common. Manifold projection enables the creation of panoramic mosaics from video sequences under more general conditions, and in particular the unrestricted motion of a hand-held camera. The panoramic mosaic is a projection of the scene into a virtual manifold whose structure depends on the cameras motion. This manifold is more general than the customary projections onto a single image plane or onto a cylinder. In addition to being more general than traditional mosaics, manifold projection is also computationally efficient, as the only image deformations used are image plane translations and rotations. Real-time, software only, implementation on a Pentium-PC, proves the superior quality and speed of this approach.

Shift-map image editing

Geometric rearrangement of images includes operations such as image retargeting, inpainting, or object rearrangement. Each such operation can be characterized by a shiftmap: the relative shift of every pixel in the output image from its source in an input image.

Omnistereo: panoramic stereo imaging

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 can be constructed by mosaicing images from a single rotating camera. This approach also enables the control of stereo disparity, giving larger baselines for faraway scenes, and a smaller baseline for closer scenes. Capturing 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 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. The lens used for omnistereo panorama is also introduced, together with the design of the mirror. Omnistereo panoramas can also be rendered by computer graphics methods to represent virtual environments.