Abstract: A computer-implemented method for recovering a digital image (x) from a sequence of observed digital images (y1, . . . , yT), includes: obtaining an observed digital image (yt); estimating a point spread function (ft) based on the observed image (yt); estimating the recovered digital image (x), based on the estimated point spread function (ft) and the observed image (yt); and repeating the above steps. In order to correct optical aberrations of a lens, a point spread function of the lens may be used.

Abstract: A computer-implemented method for recovering a digital image (x) from a sequence of observed digital images (y1, . . . , yT), includes: obtaining an observed digital image (yt); estimating a point spread function (ft) based on the observed image (yt); estimating the recovered digital image (x), based on the estimated point spread function (ft) and the observed image (yt); and repeating the above steps. In order to correct optical aberrations of a lens, a point spread function of the lens may be used.

Abstract: Data clustering is performed by executing a spectral technique, embedded within a probabilistic technique. In one embodiment, the probabilistic technique is performed by a generative model, and the spectral technique is performed within the generative model. In another embodiment, the probabilistic technique is performed by an aspect model, and the spectral technique is performed within the aspect model.

Abstract: The claimed subject matter relates to an unsupervised incremental learning framework, and in particular, to the creation and utilization of an unsupervised incremental learning framework that facilitates object discovery, clustering, characterization and/or grouping. Such an unsupervised incremental learning framework, once created, can thereafter be employed to incrementally estimate a latent variable model through the utilization of spectral and/or probabilistic models in order to incrementally cluster, discover, group and/or characterize tightly knit themes/topics within document sets and/or streams, thus leading to the generation of a set of themes/topics that better correlate with human perceptual labeling schemes.

Abstract: Data clustering is performed by executing a spectral technique, embedded within a probabilistic technique. In one embodiment, the probabilistic technique is performed by a generative model, and the spectral technique is performed within the generative model. In another embodiment, the probabilistic technique is performed by an aspect model, and the spectral technique is performed within the aspect model.