Nokia Technologies Oy

Nokia Corporation

Basavaraja S V

Mithun Uliyar

Gururaj Putraya

Veldandi Muninder

Krishna Govindarao

Ravi Shenoy

Soumik Ukil

Pushkar Prasad Patwardhan

Guruaj Gopal Putraya

Rohit Atri

Krishna Annasagar Govindarao

Gururaj Gopal Putraya

Sidharth Patil

In an example embodiment a method, apparatus and computer program product are provided. The method includes determining presence of at least one moving object in a scene based on two or more burst images corresponding to the scene captured by a first camera. One or more portions of the scene associated with the at least one moving object are identified, and, information related to the one or more portions is provided to a second camera. An image of the scene captured by the second camera second camera is received, where a pixel level shutter disposed in front of an image sensor of the second camera is programmed to periodically open and close, throughout a duration of said image capture, for pixels of the image sensor corresponding to the one or more portions of the scene. A deblurred image corresponding to the scene is generated based on the image.

Method, apparatus and computer program product for motion deblurring of images

In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of a plurality of light-field images of a scene captured in a burst capture by a light-field camera. The method includes determining shifts between images of the plurality of light-field images, where the shifts between the images of the plurality of light-field images are associated with shake of the light-field camera while capturing the plurality of light-field images. The method includes generating a plurality of depth maps for the plurality of light-field images, and generating a set of view images of the scene based on the plurality of light-field images and the plurality of depth maps. The method includes generating a refocus image by combining the set of view images based at least on the shifts between the images of the plurality of light-field images.

Method, apparatus and computer program product for image refocusing for light-field images

A method, apparatus, and computer program product for generating a seamless and error-free panorama image using a selective set of views from a multi-view image from each of the several captured light field (LF) images. The method identifies the view and corresponding image for each captured LF image which has the same or closely located center of projection with the views of neighboring LF images. Image registration and warping techniques are applied across the images and the parallax error is calculated which indicates the closeness of their center of projections. The view from each LF captured image with minimal parallax error is selected and stitched together with the other views identified as having minimal parallax error.

Robust two dimensional panorama generation using light field camera capture

In an example embodiment, method, apparatus and computer program product are provided. The method includes facilitating receipt of a deconvolved image including a plurality of component images. A guide image is selected from the component images and a cross-filtering is performed of component images other than the guide image to generate filtered component images. The cross-filtering is performed of a component image by iteratively performing, selecting a pixel and a set of neighboring pixels around the pixel in the guide image, computing a set of weights corresponding to the set of neighboring pixels based at least on spatial differences between the pixel and the set of neighboring pixels, and cross-filtering a corresponding pixel of the pixel in the component image based on the set of weights to generate a filtered corresponding pixel in the component image. The filtered component images form a filtered deconvolved image with reduced chromatic aberration.

Method, apparatus and computer program product for reducing chromatic aberrations in deconvolved images

In accordance with an example embodiment a method, apparatus and computer program product are provided. The method comprise assigning weights to at least one first feature and at least one second feature. The at least one first feature may be associated with a first image and the at least one second feature may be associated with a second image. The weights are assigned based on a distance of the at least one first feature and the at least one second feature from a central portion of the corresponding one of the first image and the second image. The method further includes registering the first image and the second image based at least on the assigned weights to determine transforms between the first image and the second image. The first image and the second image may be based on the determined transform.

Method, apparatus and computer program product for generating panorama images

In accordance with an example embodiment a method, apparatus and computer program product are provided. The method comprises facilitating a selection of a portion in at least one multimedia frame among a plurality of multimedia frames having a first resolution. The plurality of multimedia frames are partitioned into static layers and dynamic layers based on the selection of the portion. An animated image effect is configured from the dynamic layers of the plurality of multimedia frames. The method further includes performing generation of at least one dynamic frame having a second resolution from the dynamic layers, and, at least one static frame having the second resolution from the static layers. The second resolution is configured to be greater than the first resolution. An animated image having the second resolution is generated based on the at least one dynamic frame and the at least one static frame.

Method, apparatus and computer program product for generating animated images

Various methods are provided for facilitating the determination of a user attention level using facial orientation detection. One example method may include receiving an image of a user after an expiration of an inactivity timer on the user terminal. The current attention level of the user is determined based on the received image. The computing device lock condition is then set based on the determined current attention level of the user. Similar and related example apparatuses and example computer program products are also provided.

Method and apparatuses for determining a user attention level using facial orientation detection

In an example embodiment, a method, apparatus and computer program product are provided. The method includes computing a first cost volume for a light-field image. A first depth map comprising depth information of the plurality of sub-images of the light-field image is computed based on the first cost volume. A first view image comprising reconstruction information is reconstructed based on the depth information of the plurality of sub-images. A second cost volume corresponding to the first cost volume is computed based on the reconstruction information. The second cost volume is filtered based on the first view image to generate an aggregated cost volume. A second depth map is generated based on the aggregated cost volume. The second depth map facilitates generation of a second view image that is associated with a resolution higher than a resolution of the first view image.

Method, apparatus and computer program product for depth estimation

In an example embodiment, a method, apparatus and computer program product are provided. The method includes facilitating receipt of a plenoptic image associated with a scene, the plenoptic image including plenoptic micro-images and being captured by a focused plenoptic camera. The method includes generating plenoptic vectors for the plenoptic micro-images of the plenoptic image, where an individual plenoptic vector is generated for an individual plenoptic micro-image. The method includes assigning disparities for the plenoptic micro-images of the plenoptic image. A disparity for a plenoptic micro-image is assigned by accessing a plurality of subspaces associated with a set of pre-determined disparities, projecting a plenoptic vector for the plenoptic micro-image in the plurality of subspaces, calculating a plurality of residual errors based on projections of the plenoptic vector in the plurality of subspaces, and determining the disparity for the plenoptic micro-image based on a comparison of the plurality of residual errors.

Method, apparatus and computer program product for disparity estimation of plenoptic images

An apparatus for determining a pose(s) of an object(s) may include a processor and memory storing executable computer code causing the apparatus to at least perform operations including receiving a detected image of at least one face and analyzing the image of the at least one face based on data of at least one model identifying one or more poses. The poses may be related in part to at least one of a position or an orientation of respective faces. The computer program code may further cause the apparatus to determine that the face corresponds to one of the poses based in part on one or more items of data of the image passing criteria identified by the model as corresponding to the pose. Corresponding methods and computer program products are also provided.

Methods, apparatuses and computer program products for performing accurate pose estimation of objects

In an example embodiment a method, apparatus and computer program product are provided. The method includes facilitating receipt of a light-field image, determining one or more depth levels in the light-field image and generating a plurality of images from the light-field image. The method includes determining one or more registration matrices corresponding to the one or more depth levels between an image and one or more remaining images of the plurality of images. The method includes performing a super-resolution of the image and the one or more remaining images based on the one or more registration matrices to generate a super-resolved image of the image.

Method, apparatus and computer program product for generating super-resolved images

In accordance with an example embodiment a method, apparatus and computer program product are provided. The method comprises determining a plurality of first pixels having resemblance with a second pixel associated with an image based on a depth information of the image. The method further comprises replacing the second pixel by at least one first pixel of the plurality of first pixels based on a determination that the at least one first pixel is not associated with the at least one image defect.

Method, apparatus and computer program product for processing of images

In accordance with an example embodiment a method, apparatus and computer program product are provided. The method comprises facilitating receipt of a source image and compressing a plurality of images based on the source image. The plurality of images is associated with compression values generated on compression of the plurality of images. The method further comprises identifying an at least partially matching image to the source image from among the plurality of images based on the compression values.

Method, apparatus and computer program product for processing of images and compression values

In accordance with an example embodiment a method, apparatus and computer program product are provided. The method comprises extracting a set of image features from a first image and a corresponding set of image features from a second image. A first transformation matrix and a second transformation matrix are computed between the first image and the second image based on a matching of the set of image features in the first image with the corresponding set of image features in the second image. A first set of feature points is determined based on the first transformation matrix, and a second set of feature points based on the second transformation matrix. A transformation matrix is estimated between the first set of feature points and the second set of feature points. The second image is aligned with the first image based on the transformation matrix.

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Bengaluru, India

Basavaraja S V