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We propose a method for deblurring of spatially variant object motion. A principal challenge of this problem is how to estimate the point spread function (PSF) of the spatially variant blur. Based on the projective motion blur model of, we present a blur estimation technique that jointly utilizes a coded exposure camera and simple user interactions to recover the PSF. With this spatially variant PSF,...
In this paper, we propose a method for estimating the optical center of a camera given only a single image with vignetting. This is accomplished by identifying the center of the vignetting effect in the image through an analysis of semicircular tangential gradients (SCTGs). For a given image pixel, the SCTG is the image gradient along the tangential direction of the circle centered at the currently...
In this paper, we propose a method for robustly determining the vignetting function given only a single image. Our method is designed to handle both textured and untextured regions in order to maximize the use of available information. To extract vignetting information from an image, we present adaptations of segmentation techniques that locate image regions with reliable data for vignetting estimation...
We propose a novel approach to reduce spatially varying motion blur using a hybrid camera system that simultaneously captures high-resolution video at a low-frame rate together with low-resolution video at a high-frame rate. Our work is inspired by Ben-Ezra and Nayar who introduced the hybrid camera idea for correcting global motion blur for a single still image. We broaden the scope of the problem...
We derive a probabilistic similarity measure between two observed image intensities that is based on the noise properties of the camera. In many vision algorithms, the effect of camera noise is either neglected or reduced in a preprocessing stage. However, noise reduction cannot be performed with high accuracy due to lack of knowledge about the true intensity signal. Our similarity metric specifically...
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