Abstract: A method includes obtaining, using at least one processor, an input image frame. The method also includes identifying, using the at least one processor, one or more regions of the input image frame containing redundant information. In addition, the method includes performing, using the at least one processor, an image processing task using the input image frame. The image processing task is guided based on the one or more identified regions of the input image frame. The method may further include obtaining, using the at least one processor, a coarse depth map associated with the input image frame. Performing the image processing task may include refining the coarse depth map to produce a refined depth map, where the refining of the coarse depth map is guided based on the one or more identified regions of the input image frame.

Abstract: A method includes obtaining, using at least one processor, first and second input image frames, where the first and second input image frames are associated with first and second image planes, respectively. The method also includes obtaining, using the at least one processor, a depth map associated with the first input image frame. The method further includes producing another version of the depth map by performing one or more times: (a) projecting, using the at least one processor, the first input image frame to the second image plane in order to produce a projected image frame using (i) the depth map and (ii) information identifying a conversion from the first image plane to the second image plane and (b) adjusting, using the at least one processor, at least one of the depth map and the information identifying the conversion from the first image plane to the second image plane.

Abstract: A method includes obtaining, using at least one processor, an input image frame. The method also includes identifying, using the at least one processor, one or more regions of the input image frame containing redundant information. In addition, the method includes performing, using the at least one processor, an image processing task using the input image frame. The image processing task is guided based on the one or more identified regions of the input image frame. The method may further include obtaining, using the at least one processor, a coarse depth map associated with the input image frame. Performing the image processing task may include refining the coarse depth map to produce a refined depth map, where the refining of the coarse depth map is guided based on the one or more identified regions of the input image frame.

Abstract: A method includes obtaining, using first and second image sensors of an electronic device, first and second images, respectively, of a scene. The method also includes obtaining, using an image depth sensor of the electronic device, a third image and a first depth map of the scene, the first depth map having a resolution lower than a resolution of the first and second images. The method further includes undistorting the first and second images using the third image and the first depth map. The method also includes rectifying the first and second images using the third image and the first depth map. The method further includes generating a disparity map using the first and second images that have been undistorted and rectified. In addition, the method includes generating a second depth map using the disparity map and the first depth map, where the second depth map has a resolution that is higher than the resolution of the first depth map.

Abstract: A method includes obtaining a first image of a scene using a first image sensor of an electronic device and a second image of the scene using a second image sensor of the electronic device. The method also includes generating a first feature map from the first image and a second feature map from the second image. The method further includes generating a third feature map based on the first feature map, the second feature map, and an asymmetric search window. The method additionally includes generating a depth map by restoring spatial resolution to the third feature map.

Abstract: A method includes obtaining, using at least one processor, first and second input image frames, where the first and second input image frames are associated with first and second image planes, respectively. The method also includes obtaining, using the at least one processor, a depth map associated with the first input image frame. The method further includes producing another version of the depth map by performing one or more times: (a) projecting, using the at least one processor, the first input image frame to the second image plane in order to produce a projected image frame using (i) the depth map and (ii) information identifying a conversion from the first image plane to the second image plane and (b) adjusting, using the at least one processor, at least one of the depth map and the information identifying the conversion from the first image plane to the second image plane.

Abstract: A method includes determining, using at least one processor, a depth of range of focus for a scene. The method also includes determining, using the at least one processor, multiple layers associated with the scene based on the depth of focus range, where each layer is associated with image data having a different range of disparity values. The method further includes blending, using the at least one processor, the layers to produce an image having a Bokeh effect in a foreground and a background, and focused image data within the depth of focus range. The multiple layers include at least a first layer associated with the foreground, a second layer associated with the depth of focus range, and a third layer associated with the background.

Abstract: A method includes obtaining, using first and second image sensors of an electronic device, first and second images, respectively, of a scene. The method also includes obtaining, using an image depth sensor of the electronic device, a third image and a first depth map of the scene, the first depth map having a resolution lower than a resolution of the first and second images. The method further includes undistorting the first and second images using the third image and the first depth map. The method also includes rectifying the first and second images using the third image and the first depth map. The method further includes generating a disparity map using the first and second images that have been undistorted and rectified. In addition, the method includes generating a second depth map using the disparity map and the first depth map, where the second depth map has a resolution that is higher than the resolution of the first depth map.

Abstract: A method includes determining, using at least one processor, a depth of range of focus for a scene. The method also includes determining, using the at least one processor, multiple layers associated with the scene based on the depth of focus range, where each layer is associated with image data having a different range of disparity values. The method further includes blending, using the at least one processor, the layers to produce an image having a Bokeh effect in a foreground and a background, and focused image data within the depth of focus range. The multiple layers include at least a first layer associated with the foreground, a second layer associated with the depth of focus range, and a third layer associated with the background.

Abstract: A method includes obtaining a first image of a scene using a first image sensor of an electronic device and a second image of the scene using a second image sensor of the electronic device. The method also includes generating a first feature map from the first image and a second feature map from the second image. The method further includes generating a third feature map based on the first feature map, the second feature map, and an asymmetric search window. The method additionally includes generating a depth map by restoring spatial resolution to the third feature map.

Abstract: A method for 3-Dimensional scanning includes generating a plurality of depth images when a depth sensor rotates around an object. The method further includes, for each depth image: estimating a rotation (R) and a translation (T) for each depth image, using data of a Truncated Signed Distance Function (TSDF) volume; and fusing each depth image accumulatively into the TSDF volume based on the estimated R and T. An apparatus for 3-Dimensional scanning includes a depth sensor configured to generate a plurality of depth images when rotating around an object. The apparatus further includes a processor configured to, for each depth image: estimate a rotation (R) and a translation (T) for each depth image, using data of a Truncated Signed Distance Function (TSDF) volume; and fuse each depth image accumulatively into the TSDF volume based on the estimated R and T.

Abstract: A method for 3-Dimensional scanning includes generating a plurality of depth images when a depth sensor rotates around an object. The method further includes, for each depth image: estimating a rotation (R) and a translation (T) for each depth image, using data of a Truncated Signed Distance Function (TSDF) volume; and fusing each depth image accumulatively into the TSDF volume based on the estimated R and T. An apparatus for 3-Dimensional scanning includes a depth sensor configured to generate a plurality of depth images when rotating around an object. The apparatus further includes a processor configured to, for each depth image: estimate a rotation (R) and a translation (T) for each depth image, using data of a Truncated Signed Distance Function (TSDF) volume; and fuse each depth image accumulatively into the TSDF volume based on the estimated R and T.

Abstract: A compound camera system for generating an enhanced virtual image having a large depth-of-field. The compound camera system comprises a plurality of component cameras for generating image data of an object and a data processor for generating the enhanced virtual image from the image data. The data processor generates the enhanced virtual image by generating a first component virtual image at a first depth plane, generating a second component virtual image at a second depth plane, and inserting first selected pixels from the first component virtual image into enhanced the virtual image and inserting second selected pixels from the second component virtual image into the enhanced virtual image.

Abstract: A method and apparatus for inspecting specimens or patterned transmissive substrates, such as photomasks, for unwanted particles and features, particularly those associated with contacts, including irregularly shaped contacts. A specimen is illuminated by a laser through an optical system comprised of a laser scanning system, individual transmitted and/or reflected light collection optics and detectors collect and generate signals representative of the light transmitted by the substrate. The defect identification of the substrate is performed using those transmitted light signals. Defect identification is performed using an inspection algorithm by comparing image feature representations of a test specimen with a reference specimen, and using a boundary computer and flux comparison device to establish tight boundaries around contacts and compute flux differences between the test and reference specimen contacts.

Abstract: An image processing system recovers 3-D depth information for pixels of a base image representing a view of a scene. The system detects a plurality of pixels in a base image that represents a first view of a scene. The system the determines 3-D depth of the plurality of pixels in the base image by matching correspondence to a plurality of pixels in a plurality of images representing a plurality of views of the scene. The system then traces pixels in a virtual piecewise continuous depth surface by spatial propagation starting from the detected pixels in the base image by using the matching and corresponding plurality of pixels in the plurality of images to create the virtual piecewise continuous depth surface viewed from the base image, each successfully traced pixel being associated with a depth in the scene viewed from the base image.

Abstract: A compound camera system for generating an enhanced virtual image having a large depth-of-field. The compound camera system comprises a plurality of component cameras for generating image data of an object and a data processor for generating the enhanced virtual image from the image data. The data processor generates the enhanced virtual image by generating a first component virtual image at a first depth plane, generating a second component virtual image at a second depth plane, and inserting first selected pixels from the first component virtual image into enhanced the virtual image and inserting second selected pixels from the second component virtual image into the enhanced virtual image.

Abstract: A compound camera system for generating an enhanced virtual image having a large depth-of-field. The compound camera system comprises a plurality of component cameras for generating image data of an object and a data processor for generating the enhanced virtual image from the image data. The data processor generates the enhanced virtual image by generating a first component virtual image at a first depth plane, generating a second component virtual image at a second depth plane, and inserting first selected pixels from the first component virtual image into enhanced the virtual image and inserting second selected pixels from the second component virtual image into the enhanced virtual image.

Abstract: A compound camera system comprising component cameras that generate image data of an object and a processor that receives first image data from a first component camera and second image data from a second component camera and generates a virtual image. The processor projects virtual pixel data (u,v) to generate point data (x,y,z) located at depth, z=Z1, of a object plane of the object and projects the said point data (x,y,z) to generate first pixel data (u1,v1) located at a image plane of the first image. The processor also projects said point data (x,y,z) located at the depth, z=Z1, of the said object plane to generate second pixel data (u2,v2) located at the second image.

Abstract: Faster and more accurate techniques for displaying images are described. The techniques can be applied in various applications that include semiconductor fabrication processes. The invention uses preprocessed images to generate a user-selected image in order to increase the speed of image processing. The invention displays the pixels forming an image using grayscale shading in order to improve the accuracy of displaying the patterns used in photolithography processes. The techniques of the present invention can be used to display images that represent lithography patterns stored within memory devices or to display images captured by inspection or metrology devices.

Abstract: A method and apparatus for inspecting specimens or patterned transmissive substrates, such as photomasks, for unwanted particles and features, particularly those associated with contacts, including irregularly shaped contacts. A specimen is illuminated by a laser through an optical system comprised of a laser scanning system, individual transmitted and/or reflected light collection optics and detectors collect and generate signals representative of the light transmitted by the substrate. The defect identification of the substrate is performed using those transmitted light signals. Defect identification is performed using an inspection algorithm by comparing image feature representations of a test specimen with a reference specimen, and using a boundary computer and flux comparison device to establish tight boundaries around contacts and compute flux differences between the test and reference specimen contacts.