Abstract: Flare compensation includes obtaining a dark corner intensity differences profile between a first and a second image based on a relative illumination of an area outside a first image circle of the first image and a second image circle of the second image. The dark corner intensity differences profile is obtained for a luminance (Y) component. A flare profile is obtained using an intensity differences profile and the dark corner intensity differences profile. The intensity differences profile is obtained for the Y component along a stitch line between the first image and the second image. The flare profile of the Y component is converted to an RGB flare profile. The first image is modified based on the RGB flare profile to obtain a processed first image.

Abstract: Optical center calibration may include obtaining one or more parameters for optical center calibration, obtaining an input image captured by an image capture device using a lens, and determining a calibration circle using the parameters and the input image. Determining the calibration circle may include extracting rays using the input image, estimating contours using the input image and the rays, and estimating the calibration circle using the input image and the contours. The calibration may be iteratively improved by repeating calibration based on the input image and a previous iteration of optical center calibration.

Abstract: Flare compensation includes obtaining a dark corner intensity differences profile between a first and a second image based on a relative illumination of an area outside a first image circle of the first image and a second image circle of the second image. The dark corner intensity differences profile is obtained for a luminance (Y) component. A flare profile is obtained using an intensity differences profile and the dark corner intensity differences profile. The intensity differences profile is obtained for the Y component along a stitch line between the first image and the second image. The flare profile of the Y component is converted to an RGB flare profile. The first image is modified based on the RGB flare profile to obtain a processed first image.

Abstract: Flare compensation includes receiving a first image and a second image; converting the first and the second images from an RGB domain to a YUV domain; obtaining an intensity differences profile along a stitch line between the first and the second images, where the intensity differences profile is obtained for the Y component; obtaining a dark corner intensity differences profile between the first and the second images based on a relative illumination of an area outside a first image circle of the first image and a second image circle of the second image, where the dark corner intensity differences profile is obtained for the Y component; obtaining a flare profile using the intensity differences profile and the dark corner intensity differences profile; converting the flare profile of the Y component to an RGB flare profile; and modifying one of the first or second images based on the RGB flare profile.

Abstract: Image signal processing includes generating an exposure compensated image based on a gain value applied to an exposure level of a first image and a gain value applied to an exposure level of a second image. The gain value may be progressively increased from an approximate center of the first image to an edge of the first image to a common exposure level. The gain value may be progressively decreased from an approximate center of the second image to an edge of the second image to the common exposure level. Gain values may be scaled on each color channel for a pixel based on a saturation level of the pixel.

Abstract: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

Abstract: Optical center calibration may include obtaining one or more parameters for optical center calibration, obtaining an input image captured by an image capture device using a lens, and determining a calibration circle using the parameters and the input image. Determining the calibration circle may include extracting rays using the input image, estimating contours using the input image and the rays, and estimating the calibration circle using the input image and the contours. The calibration may be iteratively improved by repeating calibration based on the input image and a previous iteration of optical center calibration.

Abstract: Flare compensation includes receiving a first image and a second image; converting the first and the second images from an RGB domain to a YUV domain; obtaining an intensity differences profile along a stitch line between the first and the second images, where the intensity differences profile is obtained for the Y component; obtaining a dark corner intensity differences profile between the first and the second images based on a relative illumination of an area outside a first image circle of the first image and a second image circle of the second image, where the dark corner intensity differences profile is obtained for the Y component; obtaining a flare profile using the intensity differences profile and the dark corner intensity differences profile; converting the flare profile of the Y component to an RGB flare profile; and modifying one of the first or second images based on the RGB flare profile.

Abstract: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

Abstract: Image signal processing includes generating an exposure compensated image based on a gain value applied to an exposure level of a first image and a gain value applied to an exposure level of a second image. The gain value may be progressively increased from an approximate center of the first image to an edge of the first image to a common exposure level. The gain value may be progressively decreased from an approximate center of the second image to an edge of the second image to the common exposure level. Gain values may be scaled on each color channel for a pixel based on a saturation level of the pixel.

Abstract: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

Abstract: Image analysis and processing may include using an image processor to receive image data corresponding to an input image, determine an initial gain value for the image data based on at least one of a two-dimensional gain map or a parameterized radial gain model, determine whether the initial gain value is below a threshold, determine a maximum RGB triplet value for the image data where the initial gain value is below the threshold, determine a pixel intensity as output of a function for saturation management, determine a final gain value for the image data based on the maximum RGB triplet value and the pixel intensity, apply the final gain value against the image data to produce processed image data, and output the processed image data for further processing using the image processor.

Abstract: Methods and apparatus for generating network of endoluminal surfaces by defining a set of medial axes for a tubular structure, defining a series of cross sections along medial axis in the set of medial axes, generating a connectivity graph of the medial axes, defining multiple surface representations based upon the graph of the medial axes and the cross sections, computing a volume defined by a first one of the surface representations, defining a partition of the medial axis, cross-sections, surface and/or volume representations, and outputting the network of endoluminal surfaces.