Abstract: A method, computer program product, and computer system for identifying a first portion of a facial image in a first image, wherein the first portion includes noise. A corresponding portion of the facial image is identified in a second image, wherein the corresponding portion includes less noise than the first portion. One or more filter parameters of the first portion are determined based upon, at least in part, the first portion and the corresponding portion. At least a portion of the noise from the first portion is smoothed based upon, at least in part, the one or more filter parameters. At least a portion of face specific details from the corresponding portion is added to the first portion.

Abstract: Methods, systems, and articles of manufacture for detecting placeholder images are disclosed. These include, accessing a collection of digital images, clustering the digital images to generate at least one of a plurality of exact-duplicate image clusters and a plurality of near-duplicate image clusters, and selecting one or more placeholder image clusters from at least one of the plurality of exact-duplicate image clusters or the plurality of near-duplicate image clusters.

Abstract: An exemplary illumination normalization method is provided which includes receiving an input image having at least one spurious edge directly resulting from illumination, performing anisotropic diffusion on the input image to form a diffusion image, and removing at the least one spurious edge using the diffusion image. Another embodiment consistent with the invention is an apparatus for performing illumination normalization in an image which includes a processor operably coupled to a memory storing input image data which contains an object of interest having at least one spurious edge directly resulting from illumination, a model of a representative object of interest, and functional processing units for controlling image processing, wherein the functional processing units further include a model based anisotropic diffusion module which predicts edge information regarding the object of interest based upon the model, and produces a reflectance estimation utilizing the predicted edge information.

Abstract: A sequence of images depicts a foreground object in motion. A base image is selected, and the other images in the sequence are co-registered with the base image in order to align the images to a common coordinate system. A background image and a binary foreground mask are generated from the sequence of aligned images. By applying the foreground mask to a chosen one of the aligned images, a representation of the moving object is extracted. After blurring the background image, the extracted representation may be superimposed onto the blurred background image to produce a new image.

Abstract: Methods and apparatuses for image processing are presented. An exemplary method is provided which provides a model which includes information not found in the digital image, accessing digital image data and the model, and performing anisotropic diffusion on the digital image data utilizing the model. An apparatus for processing a digital image is presented which includes a processor operably coupled to memory storing digital image data, a model which includes information not found in the digital image data, and functional processing units for controlling image processing, where the functional processing units include a model generation module, and a model-based anisotropic diffusion module which performs anisotropic diffusion on the digital image data utilizing the information provided by the model.

Abstract: An exemplary illumination normalization method is provided which includes receiving an input image having at least one spurious edge directly resulting from illumination, performing anisotropic diffusion on the input image to form a diffusion image, and removing at the least one spurious edge using the diffusion image. Another embodiment consistent with the invention is an apparatus for performing illumination normalization in an image which includes a processor operably coupled to a memory storing input image data which contains an object of interest having at least one spurious edge directly resulting from illumination, a model of a representative object of interest, and functional processing units for controlling image processing, wherein the functional processing units further include a model based anisotropic diffusion module which predicts edge information regarding the object of interest based upon the model, and produces a reflectance estimation utilizing the predicted edge information.

Abstract: A method for image relighting is presented which receives an input image having at least one spurious edge directly resulting from a first illumination present when the input image was acquired, performs anisotropic diffusion on the input image to form a diffusion image, removes the first illumination using the diffusion image to generate a reflectance image and applies a second illumination to the reflectance image. An apparatus for relighting is presented which includes a processor operably coupled to memory storing input image having a first illumination present when the input image was acquired, and functional processing including an anisotropic diffusion module to perform anisotropic diffusion on the input image to form a diffusion image, a combination module which removes the first illumination using the diffusion image to generate a reflectance image, a second illumination module which generates a second illumination, and a lighting application model which applies the second illumination.

Abstract: A sequence of images depicts a foreground object in motion. A base image is selected, and the other images in the sequence are co-registered with the base image in order to align the images to a common coordinate system. A background image and a binary foreground mask are generated from the sequence of aligned images. By applying the foreground mask to a chosen one of the aligned images, a representation of the moving object is extracted. After blurring the background image, the extracted representation may be superimposed onto the blurred background image to produce a new image.