Abstract: Systems and methods for psycho-signal processing. According to an aspect, a method includes receiving a visual representation of a subject. The method also includes performing a structured motion operation on the received visual representation to generate a modified visual representation of the subject. The method further includes presenting, via a user interface, the modified visual representation.

Abstract: A method of mesoscopic photogrammetry can be carried out using a set of images captured from a camera on a mobile computing device. Upon receiving the set of images, the method generates a composite image, which can include applying homographic rectification to warp all images of the set of images onto a common plane; applying a rectification model to undo perspective distortion in each image of the set of images; and applying an undistortion model for adjusting for camera imperfections of a camera that captured each image of the set of images. A height map is generated co-registered with the composite image, for example, by using an untrained CNN whose weights/parameters are optimized in order to optimize the height map. The height map and the composite image can be output for display.

Abstract: Systems and methods for imaging based on multiple cross-sectional images acquired at different angles are disclosed. According to an aspect, multiple cross-sectional images of an object are acquired at different angles. The method also includes registering the acquired cross-sectional images. Further, the method includes reconstructing an enhanced resolution image of the object based on the registered images. As a result of registering the images, a distortion map is generated that is coregistered with the high-resolution image. The method also includes displaying an image of the object based on the enhanced resolution image and the distortion map.

Abstract: Systems and methods for imaging based on multiple cross-sectional images acquired at different angles are disclosed. According to an aspect, multiple cross-sectional images of an object are acquired at different angles. The method also includes registering the acquired cross-sectional images. Further, the method includes reconstructing an enhanced resolution image of the object based on the registered images. As a result of registering the images, a distortion map is generated that is coregistered with the high-resolution image. The method also includes displaying an image of the object based on the enhanced resolution image and the distortion map.

Abstract: Systems and methods for psycho-signal processing. According to an aspect, a method includes receiving a visual representation of a subject. The method also includes performing a structured motion operation on the received visual representation to generate a modified visual representation of the subject. The method further includes presenting, via a user interface, the modified visual representation.

Abstract: Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.

Abstract: Segmentation and identification of closed-contour features in images using graph theory and quasi-polar transform are disclosed. According to an aspect, a method includes representing, in a rectangular domain, an image including a feature of interest. Further, the method includes determining a point within the feature of interest. The method also includes transforming the image of the feature from the rectangular domain to a quasi-polar domain based on the point. The quasi-polar domain is represented as a graph of nodes connected together by edges. The method also includes graph cutting the quasi-polar domain to identify the boundary of the feature of interest in the image.

Abstract: Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.

Abstract: Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.

Abstract: Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.

Abstract: Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.

Abstract: Segmentation and identification of closed-contour features in images using graph theory and quasi-polar transform are disclosed. According to an aspect, a method includes representing, in a rectangular domain, an image including a feature of interest. Further, the method includes determining a point within the feature of interest. The method also includes transforming the image of the feature from the rectangular domain to a quasi-polar domain based on the point. The quasi-polar domain is represented as a graph of nodes connected together by edges. The method also includes graph cutting the quasi-polar domain to identify the boundary of the feature of interest in the image.

Abstract: Imaging and visualization systems, instruments, and methods using optical coherence tomography (OCT) are disclosed. A method for OCT image capture includes determining a location of a feature of interest within an operative field. The method also includes determining a relative positioning between the feature of interest and an OCT scan location. Further, the method includes controlling capture of an OCT image at a set position relative to the feature of interest based on the relative positioning.

Abstract: Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.

Abstract: Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.

Abstract: Imaging and visualization systems, instruments, and methods using optical coherence tomography (OCT) are disclosed. A method for OCT image capture includes determining a location of a feature of interest within an operative field. The method also includes determining a relative positioning between the feature of interest and an OCT scan location. Further, the method includes controlling capture of an OCT image at a set position relative to the feature of interest based on the relative positioning.

Abstract: Disclosed herein are systems and method for segmentation and identification of structured features in images. According to an aspect, a method may include representing an image as a graph of nodes connected together by edges. For example, the image may be an ocular image showing layered structures or other features of the retina. The method may also include adding, to the graph, nodes adjacent to nodes along first and second sides of the graph. The added nodes may have edge weights less than the nodes along the first and second sides of the graph. Further, the method may include assigning start and end points to any of the added nodes along the first and second sides, respectively. The method may also include graph cutting between the start and end points for identifying a feature in the image.

Abstract: A method of creating a super-resolved color image from multiple lower-resolution color images is provided by combining a data fidelity penalty term, a spatial luminance penalty term, a spatial chrominance penalty term, and an inter-color dependencies penalty term to create an overall cost function. The data fidelity penalty term is an L1 norm penalty term to enforce similarities between raw data and a high-resolution image estimate, the spatial luminance penalty term is to encourage sharp edges in a luminance component to the high-resolution image, the spatial chrominance penalty term is to encourage smoothness in a chrominance component of the high-resolution image, and the inter-color dependencies penalty term is to encourage homogeneity of an edge location and orientation in different color bands. A steepest descent optimization is applied to the overall cost function for minimization by applying a derivative to each color band while the other color bands constant.

Abstract: A computer method of creating a super-resolved grayscale image from lower-resolution images using an L1 norm data fidelity penalty term to enforce similarities between low and a high-resolution image estimates is provided. A spatial penalty term encourages sharp edges in the high-resolution image, the data fidelity penalty term is applied to space invariant point spread function, translational, affine, projective and dense motion models including fusing the lower-resolution images, to estimate a blurred higher-resolution image and then a deblurred image. The data fidelity penalty term uses the L1 norm in a likelihood fidelity term for motion estimation errors. The spatial penalty term uses bilateral-TV regularization with an image having horizontal and vertical pixel-shift terms, and a scalar weight between 0 and 1. The penalty terms create an overall cost function having steepest descent optimization applied for minimization. Direct image operator effects replace matrices for speed and efficiency.

Abstract: An integrated method for both super-resolution and multi-frame demosaicing includes an image fusion followed by simultaneous deblurring and interpolation. For the case of color super-resolution, the first step involves application of recursive image fusion separately on the three different color layers. The second step is based on minimizing a maximum a posteriori (MAP) cost function. In one embodiment, the MAP cost function is composed of several terms: a data fidelity penalty term that penalizes dissimilarity between the raw data and the super-resolved estimate, a luminance penalty term that favors sharp edges in the luminance component of the image, a chrominance penalty term that favors low spatial frequency changes in the chrominance component of the image, and an orientation penalty term that favors similar edge orientations across the color channels. The method is also applicable to color super-resolution (without demosaicing), where the low-quality input images are already demosaiced.