Abstract: In accordance with at least some embodiments of the present disclosure, a process for generating a depth map for converting a two-dimensional (2D) image to a three-dimensional (3D) image is described. The process may include generating a depth gradient map from the 2D image, wherein the depth gradient map is configured to associate one or more edge counts with one or more depth values, extracting an image component from the 2D image, wherein the image component is associated with a color component in a color space, determining a set of gains to adjust the depth gradient map based on the image component, and generating the depth map by performing depth fusion based on the depth gradient map and the set of gains.

Abstract: In accordance with at least some embodiments of the present disclosure, a process for converting a two-dimensional (2D) image based on edge information is described. The process may include partitioning the 2D image to generate a plurality of blocks, segmenting the plurality of blocks into a group of regions based on edges determined in the plurality of blocks, assigning depth values to the plurality of blocks based on a depth gradient hypothesis associated with the group of regions, wherein pixels in each of the plurality of blocks are associated with a same depth value, and generating the depth map based on the depth values of the plurality of blocks.

Abstract: A system and method of correcting a depth map for 3D image is disclosed. A spatial spectral transform unit extracts pixels of object boundaries according to an input image, wherein the spatial spectral transform unit adopts Hilbert-Huang transform (HHT). A correction unit corrects an input depth map corresponding to the input image according to the pixels of object boundaries, thereby resulting in an output depth map.

Abstract: In accordance with at least some embodiments of the present disclosure, a process for enhancing a two-dimensional (2D) image is described. The process may include generating a depth map for the 2D image based on edges detected from the 2D image, wherein the depth map can be used to convert the 2D image to a 3D image. The process may further include enhancing the 2D image using the depth map.

Abstract: A method of revising depth of a three-dimensional (3D) image is disclosed. The method comprises the following steps: firstly, at least one initial depth map associated with one image of the 3D image pair based on stereo matching technique is received, wherein the one image comprises a plurality of pixels, and the initial depth map carries an initial depth value of each pixel. Then, the inconsistence among the pixels of the one image of the 3D image pair is detected to estimate a reliable map. Finally, the initial depth value is interpolated according to the reliable map and the proximate pixels, so as to generate a revised depth map by revising the initial depth value.

Abstract: A nonlinear depth remapping method includes the following steps: firstly, an initial depth map associated with at least one image is received, with the image comprising a plurality of pixels and the initial depth map carrying an initial depth value of each pixel. Then, an exponential function is utilized to adjust the initial depth values, so as to generate an adjusted depth map.

Abstract: In accordance with at least some embodiments of the present disclosure, a processor for performing stereo matching of a first image and a second image is described. The processor may include a first pipeline stage configured to generate data costs associated with a first tile selected from the first image, wherein the data costs is generated based on pixels in the first tile and corresponding pixels in the second image. The processor may include a second pipeline stage configured to generate disparity values associated with the first tile and an outbound message from the first tile to one of neighboring tiles in the first image, wherein the disparity values and the outbound message are generated based on the data costs and inbound messages from the neighboring tiles to the first tile.

Abstract: A depth-based image enhancement system is disclosed. A depth estimation unit generates three-dimensional (3D) depth information from a two-dimensional (2D) image. Subsequently, an image enhancement unit enhances the 2D image according to the 3D depth information.

Abstract: A system of generating three-dimensional (3D) depth information is disclosed. A color and object independent local blurriness estimation unit analyzes blurriness of each pixel of a two-dimensional (2D) image. Subsequently, a depth assignment unit assigns depth information to the 2D image according to the analyzed blurriness.

Abstract: In accordance with at least some embodiments of the present disclosure, a process for generating a depth map for converting a two-dimensional (2D) image to a three-dimensional (3D) image is described. The process may include generating a depth gradient map from the 2D image, wherein the depth gradient map is configured to associate one or more edge counts with one or more depth values, extracting an image component from the 2D image, wherein the image component is associated with a color component in a color space, determining a set of gains to adjust the depth gradient map based on the image component, and generating the depth map by performing depth fusion based on the depth gradient map and the set of gains.

Abstract: A system and method of generating three-dimensional (3D) depth information is disclosed. A classification and segmentation unit segments a two-dimensional (2D) image into a number of segments, such that pixels having similar characteristics are classified into the same segment. A spatial-domain texel density analysis unit performs texel density analysis on the 2D image to obtain textual density. A depth assignment unit assigns depth information to the 2D image according to the analyzed textual density.

Abstract: A system of generating three-dimensional (3D) depth information is disclosed. A color and object independent local blurriness estimation unit analyzes blurriness of each pixel of a two-dimensional (2D) image. Subsequently, a depth assignment unit assigns depth information to the 2D image according to the analyzed blurriness.