Abstract: Systems and methods are disclosed for high dynamic range anti-ghosting and fusion. For example, methods may include receiving images from image sensors in a linear domain, each image having different exposures or gains, blending luminance values at each pixel from each of the images to generate a blended image, selecting an useful image based on degree of useful information for a pixel, calculating a distance value from the images for the pixel, locating from a look-up table (LUT) an anti-ghosting weight using the useful image and the distance value for the pixel, proportionally applying the located anti-ghosting weight to the pixel for each of the input images to generate an output image, all being performed in the linear domain, and storing, displaying, or transmitting the output image based on at least the anti-ghosting weight.

Abstract: Field variable tone mapping is performed for 360 content. An image capture device includes an image sensor and a processor. The image sensor obtains a hyper-hemispherical image and the processor performs local tone mapping (LTM) on a first area of the hyper-hemispherical image and performs global tone mapping (GTM) on a second area of the hyper-hemispherical image to obtain a processed image. The processor may be configured to display, store, output, or transmit the processed image.

Abstract: Image capture devices and methods may use field variable tone mapping for 360 content. An image capture device may comprise an image sensor and a processor. The image sensor may capture a hyper-hemispherical image that includes an image circle portion. The processor may perform local tone mapping (LTM) on a first area of the image circle portion and perform global tone mapping (GTM) on a second area of pixels of the image circle portion. The GTM may be performed on a condition that a portion of a predefined area of pixels overlaps with a stitch line. The processor may be configured to stitch the hyper-hemispherical image and a second hyper-hemispherical image at the stitch line to obtain a processed image. The processor may be configured to display, store, output, or transmit the processed image.

Abstract: Systems and methods are disclosed for non-linear color correction. The method including receiving an input image from an image sensor, converting the input image from a red, green, blue (RGB) color space format to an alternate color space format, determining localized hue correction parameters for a selected color in the alternate color space, determining localized saturation correction parameters for a selected hue in the alternate color space, applying the localized hue correction parameters and the localized saturation correction parameters to the input image to generate an output image and storing, displaying, or transmitting the output image based on at least the localized hue correction parameters and the localized saturation correction parameters.

Abstract: Scene-based automatic white balance correction may be performed by obtaining an input image. A histogram may be computed from an image thumbnail. The scene-based automatic white balance correction may include classifying a scene. The scene-based automatic white balance correction may include learning a first filter and a second filter. The first filter and the second filter may be learned from one or several different instances of the raw image thumbnail, the augmented image thumbnail, the scene classification, or any combination thereof. The scene-based automatic white balance correction may include applying the filter to the histogram to determine white balance correction coefficients and obtain a processed image.

Abstract: A method and apparatus may be used for performing a scene-based automatic white balance correction. The method may include obtaining an input image. The method may include obtaining a raw image thumbnail. The method may include obtaining an augmented image thumbnail. The method may include computing a histogram from an image thumbnail. The method may include determining a scene classification. The method may include learning a filter. The filter may be learned from one or several different instances of the raw image thumbnail, the augmented image thumbnail, the scene classification, or any combination thereof. The method may include applying the filter to the histogram to determine white balance correction coefficients and obtain a processed image.

Abstract: Image capture devices and methods may use field variable tone mapping for 360 content. An image capture device may comprise an image sensor and a processor. The image sensor may capture a hyper-hemispherical image that includes an image circle portion. The processor may perform local tone mapping (LTM) on a first area of the image circle portion and perform global tone mapping (GTM) on a second area of pixels of the image circle portion. The GTM may be performed on a condition that a portion of a predefined area of pixels overlaps with a stitch line. The processor may be configured to stitch the hyper-hemispherical image and a second hyper-hemispherical image at the stitch line to obtain a processed image. The processor may be configured to display, store, output, or transmit the processed image.

Abstract: Image capture devices and methods may use field variable tone mapping for 360 content. An image capture device may comprise an image sensor and a processor. The image sensor may capture a hyper-hemispherical image that includes an image circle portion and a dark corner portion. The processor may perform local tone mapping (LTM) on a first area of the image circle portion and perform global tone mapping (GTM) on a second area of pixels of the image circle portion. The GTM may be performed on a condition that a portion of a predefined area of pixels overlaps with a stitch line. The processor may be configured to stitch the hyper-hemispherical image and a second hyper-hemispherical image at the stitch line to obtain a processed image. The processor may be configured to display, store, output, or transmit the processed image.

Abstract: Image analysis includes obtaining, from an image signal processor, image processing information corresponding to a previously processed image, obtaining scene classification information for an input image based on the image processing information, generating a processed image by processing the input image based on the scene classification information, and outputting the processed image. The image processing information includes automatic white balance correction information and obtaining the scene classification information includes obtaining the scene classification information based on the automatic white balance correction information.

Abstract: Systems and methods are disclosed for high dynamic range anti-ghosting and fusion. For example, methods may include receiving images from image sensors in a linear domain, each image having different exposures or gains, blending luminance values at each pixel from each of the images to generate a blended image, selecting an useful image based on degree of useful information for a pixel, calculating a distance value from the images for the pixel, locating from a look-up table (LUT) an anti-ghosting weight using the useful image and the distance value for the pixel, proportionally applying the located anti-ghosting weight to the pixel for each of the input images to generate an output image, all being performed in the linear domain, and storing, displaying, or transmitting the output image based on at least the anti-ghosting weight.

Abstract: Systems and methods are disclosed for non-linear color correction. The method including receiving an input image from an image sensor, converting the input image from a red, green, blue (RGB) color space format to an alternate color space format, determining localized hue correction parameters for a selected color in the alternate color space, determining localized saturation correction parameters for a selected hue in the alternate color space, applying the localized hue correction parameters and the localized saturation correction parameters to the input image to generate an output image and storing, displaying, or transmitting the output image based on at least the localized hue correction parameters and the localized saturation correction parameters.

Abstract: Systems and methods are disclosed for image signal processing. For example, methods may include receiving an image from an image sensor, detecting, in a linear domain, color fringing areas in the image, correcting detected color fringing areas to obtain a corrected image, performing tone mapping to the corrected image to obtain a tone mapped image and storing, displaying, or transmitting an output image based on at least the tone mapped image.

Abstract: Image capture devices and methods may use field variable tone mapping for 360 content. An image capture device may comprise an image sensor and a processor. The image sensor may capture a hyper-hemispherical image that includes an image circle portion and a dark corner portion. The processor may perform local tone mapping (LTM) on a first area of the image circle portion and perform global tone mapping (GTM) on a second area of pixels of the image circle portion. The GTM may be performed on a condition that a portion of a predefined area of pixels overlaps with a stitch line. The processor may be configured to stitch the hyper-hemispherical image and a second hyper-hemispherical image at the stitch line to obtain a processed image. The processor may be configured to display, store, output, or transmit the processed image.

Abstract: Systems and methods are disclosed for non-linear color correction. The method including receiving an input image from an image sensor, converting the input image from a red, green, blue (RGB) color space format to an alternate color space format, determining localized hue correction parameters for a selected color in the alternate color space, determining localized saturation correction parameters for a selected hue in the alternate color space, applying the localized hue correction parameters and the localized saturation correction parameters to the input image to generate an output image and storing, displaying, or transmitting the output image based on at least the localized hue correction parameters and the localized saturation correction parameters.

Abstract: A method and apparatus may be used for performing a scene-based automatic white balance correction. The method may include obtaining an input image. The method may include obtaining a raw image thumbnail. The method may include obtaining an augmented image thumbnail. The method may include computing a histogram from an image thumbnail. The method may include determining a scene classification. The method may include learning a filter. The filter may be learned from one or several different instances of the raw image thumbnail, the augmented image thumbnail, the scene classification, or any combination thereof. The method may include applying the filter to the histogram to determine white balance correction coefficients and obtain a processed image.

Abstract: Image analysis includes obtaining, from an image signal processor, image processing information corresponding to a previously processed image, obtaining scene classification information for an input image based on the image processing information, generating a processed image by processing the input image based on the scene classification information, and outputting the processed image. The image processing information includes automatic white balance correction information and obtaining the scene classification information includes obtaining the scene classification information based on the automatic white balance correction information.

Abstract: An achromatic 3D STED measuring optical process and optical method, based on a conical diffraction effect or an effect of propagation of light in uniaxial crystals, including a cascade of at least two uniaxial or conical diffraction crystals creating, from a laser source, all of the light propagating along substantially the same optical path, from the output of an optical bank to the objective of a microscope. A spatial position of at least one luminous nano-emitter, structured object or a continuous distribution in a sample is determined. Reconstruction of the sample and its spatial and/or temporal and/or spectral properties is treated as an inverse Bayesian problem leading to the definition of an a posteriori distribution, and a posteriori relationship combining, by virtue of the Bayes law, the probabilistic formulation of a noise model, and possible priors on a distribution of light created in the sample by projection.

Abstract: Image analysis includes obtaining, from an image signal processor, image processing information corresponding to a previously processed image, obtaining scene classification information for an input image based on the image processing information, generating a processed image by processing the input image based on the scene classification information, and outputting the processed image. The image processing information includes automatic white balance correction information and obtaining the scene classification information includes obtaining the scene classification information based on the automatic white balance correction information.

Abstract: An achromatic 3D STED measuring optical process and optical method, based on a conical diffraction effect or an effect of propagation of light in uniaxial crystals, including a cascade of at least two uniaxial or conical diffraction crystals creating, from a laser source, all of the light propagating along substantially the same optical path, from the output of an optical bank to the objective of a microscope. A spatial position of at least one luminous nano-emitter, structured object or a continuous distribution in a sample is determined. Reconstruction of the sample and its spatial and/or temporal and/or spectral properties is treated as an inverse Bayesian problem leading to the definition of an a posteriori distribution, and a posteriori relationship combining, by virtue of the Bayes law, the probabilistic formulation of a noise model, and possible priors on a distribution of light created in the sample by projection.

Abstract: Systems and methods are disclosed for non-linear color correction. The method including receiving an input image from an image sensor, converting the input image from a red, green, blue (RGB) color space format to an alternate color space format, determining localized hue correction parameters for a selected color in the alternate color space, determining localized saturation correction parameters for a selected hue in the alternate color space, applying the localized hue correction parameters and the localized saturation correction parameters to the input image to generate an output image and storing, displaying, or transmitting the output image based on at least the localized hue correction parameters and the localized saturation correction parameters.