Abstract: According to an example embodiment, a stereoscopic near-eye display (NED) assembly for a NED device including a pair of NED assemblies is provided, where an input image is subjected to a preprocessing procedure that applies image-area-position dependent preprocessing before providing it for viewing through a lens assembly that comprises a diffractive optical element (DOE) arranged to provide a phase delay that is different through a plurality of positions of its aperture. According to other example embodiments, an apparatus and a method for deriving the preprocessing procedure and the DOE are provided.

Abstract: According to an example embodiment, a stereoscopic near-eye display (NED) assembly for a NED device including a pair of NED assemblies is provided, where an input image is subjected to a preprocessing procedure that applies image-area-position dependent preprocessing before providing it for viewing through a lens assembly that comprises a diffractive optical element (DOE) arranged to provide a phase delay that is different through a plurality of positions of its aperture. According to other example embodiments, an apparatus and a method for deriving the preprocessing procedure and the DOE are provided.

Abstract: Various embodiments are provided which relate to the field of image signal processing, specifically relating to the generation of a depth-view image of a scene from a set of input images of a scene taken at different cameras of a multi-view imaging system. A method comprises obtaining a frame of an image of a scene and a frame of a depth map regarding the frame of the image. A minimum depth and a maximum depth of the scene and a number of depth layers for the depth map are determined. Pixels of the image are projected to the depth layers to obtain projected pixels on the depth layers; and cost values for the projected pixels are determined. The cost values are filtered and a filtered cost value is selected from a layer to obtain a depth value of a pixel of an estimated depth map.

Abstract: It is inter alia disclosed a method comprising: projecting a distance value and position associated with each of a plurality of pixels of an array of pixel sensors in an image sensor of a time of flight camera system onto a three dimensional world coordinate space as a plurality of depth sample points; and merging pixels from a two dimensional camera image with the plurality of depth sample points of the three dimensional world coordinate space to produce a fused two dimensional depth mapped image.

Abstract: It is inter alia disclosed to determine a phase difference between a light signal transmitted by a time of flight camera system and a reflected light signal received by at least one pixel sensor of an array of pixel sensors in an image sensor of the time of flight camera system, wherein the reflected light signal received by the at least one pixel sensor is reflected from an object illuminated by the transmitted light signal (301); determine an amplitude of the reflected light signal received by the at least one pixel sensor (301); combine the amplitude and phase difference for the at least one pixel sensor into a combined signal parameter for the at least one pixel sensor (307); and de-noise the combined signal parameter for the at least one pixel sensor by filtering with a filter the combined parameter for the at least one pixel sensor (309).

Abstract: In an example embodiment, a method, apparatus and computer program product are provided. The method includes computing a cost volume associated with a reference image. Down-sampling of the cost volume and the reference image into at least one level is performed to generate at least one down-sampled cost volume and at least one down-sampled reference image, respectively. An up-sampling of the at least one down-sampled cost volume and the at least one down-sampled reference image into the at least one level is performed to generate at least one up-sampled cost volume and at least one up-sampled reference image, respectively. A color weight map associated with the cost volume and the at least one down-sampled cost volume is computed based on the reference image and the at least one down-sampled reference image at the at least one level. Aggregated cost volume is determined based at least on the color weight map.

Abstract: Various embodiments are provided which relate to the field of image signal processing, specifically relating to the generation of a depth-view image of a scene from a set of input images of a scene taken at different cameras of a multi-view imaging system. A method comprises obtaining a frame of an image of a scene and a frame of a depth map regarding the frame of the image. A minimum depth and a maximum depth of the scene and a number of depth layers for the depth map are determined. Pixels of the image are projected to the depth layers to obtain projected pixels on the depth layers; and cost values for the projected pixels are determined. The cost values are filtered and a filtered cost value is selected from a layer to obtain a depth value of a pixel of an estimated depth map.

Abstract: A method, apparatus, and computer program product are disclosed for compression of a 2D-plus-depth representation based on spatial downsampling of an initial depth map. By utilizing the color image accompanying the initial depth map, it is possible to infer structural information that refines and reconstructs the initial depth map out of a heavily subsampled version of the depth map. In the process, no indexing of the exact positions of the subsampled depth values is needed, which leads to very efficient compression. An example method causes segmentation of the color image into a set of super-pixel segments, and causes downsampling of the initial depth map based on the set of super-pixel segments. The method subsequently causes generation and storage of a compressed representation based on the segmented color image and the downsampled depth map. A corresponding apparatus and computer program product are also provided.

Abstract: In an example embodiment, a method, apparatus and computer program product are provided. The method includes computing a cost volume associated with a reference image. Down-sampling of the cost volume and the reference image into at least one level is performed to generate at least one down-sampled cost volume and at least one down-sampled reference image, respectively. An up-sampling of the at least one down-sampled cost volume and the at least one down-sampled reference image into the at least one level is performed to generate at least one up-sampled cost volume and at least one up-sampled reference image, respectively. A color weight map associated with the cost volume and the at least one down-sampled cost volume is computed based on the reference image and the at least one down-sampled reference image at the at least one level. Aggregated cost volume is determined based at least on the color weight map.

Abstract: A method, apparatus, and computer program product are disclosed for compression of a 2D-plus-depth representation based on spatial downsampling of an initial depth map. By utilizing the color image accompanying the initial depth map, it is possible to infer structural information that refines and reconstructs the initial depth map out of a heavily subsampled version of the depth map. In the process, no indexing of the exact positions of the subsampled depth values is needed, which leads to very efficient compression. An example method causes segmentation of the color image into a set of super-pixel segments, and causes downsampling of the initial depth map based on the set of super-pixel segments. The method subsequently causes generation and storage of a compressed representation based on the segmented color image and the downsampled depth map. A corresponding apparatus and computer program product are also provided.

Abstract: It is inter alia disclosed a method comprising: projecting a distance value and position associated with each of a plurality of pixels of an array of pixel sensors in an image sensor of a time of flight camera system onto a three dimensional world coordinate space as a plurality of depth sample points; and merging pixels from a two dimensional camera image with the plurality of depth sample points of the three dimensional world coordinate space to produce a fused two dimensional depth mapped image.

Abstract: It is inter alia disclosed to determine a phase difference between a light signal transmitted by a time of flight camera system and a reflected light signal received by at least one pixel sensor of an array of pixel sensors in an image sensor of the time of flight camera system, wherein the reflected light signal received by the at least one pixel sensor is reflected from an object illuminated by the transmitted light signal (301); determine an amplitude of the reflected light signal received by the at least one pixel sensor (301); combine the amplitude and phase difference for the at least one pixel sensor into a combined signal parameter for the at least one pixel sensor (307); and de-noise the combined signal parameter for the at least one pixel sensor by filtering with a filter the combined parameter for the at least one pixel sensor (309).

Abstract: An apparatus comprising: an image analyser configured to analyse at least two images to determine at least one matched feature; a camera definer configured to determine at least two difference parameters between the at least two images; and a rectification determiner configured to determine values for the at least two difference parameters in an error search using an error criterion based on the at least one matched feature in the at least two images and an estimated difference parameter value, wherein the value for each difference parameter is determined serially.

Abstract: There is provided methods, apparatuses and computer program products for image processing in which a pair of images may be downsampled to lower resolution pair of images and further to obtain a disparity image representing estimated disparity between at least a subset of pixels in the pair of images. A confidence of the disparity estimation may be obtained and inserted into a confidence map. The disparity image and the confidence map may be filtered jointly to obtain a filtered disparity image and a filtered confidence map by using a spatial neighborhood of the pixel location. An estimated disparity distribution of the pair of images may be obtained through the filtered disparity image and the confidence map.