Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to image enhancement using a generative adversarial network (GAN). The computer-implemented system can comprise a memory that can store computer-executable components. The computer-implemented system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise a training component that can train a discriminator of the GAN to score a texture of a CT image, wherein the texture can be derived from a difference of two conditionally independent estimates produced by a generator of the GAN by respectively processing two independent noisy samples of images.

Abstract: Systems and methods for computed tomography imaging are provided. In one embodiment, a method includes acquiring an image, inputting the image to a machine learning model to generate a denoised image, the machine learning model trained with a loss function that weights variance differently from bias, and outputting the denoised image. In this way, structural details in denoised CT images may be improved while maintaining textural information in the denoised images.

Abstract: The present approach relates to the training of a machine learning algorithm for image generation and use of such a trained algorithm for image generation. Training the machine learning algorithm may involve using multiple images produced from a single set of tomographic projection or image data (such as a simple reconstruction and a computationally intensive reconstruction), where one image is the target image that exhibits the desired characteristics for the final result. The trained machine learning algorithm may be used to generate a final image corresponding to a computationally intensive algorithm from an input image generated using a less computationally intensive algorithm.

Abstract: Light reflected from an illuminated object is mixed with a reference beam and sensed at a sensor array of a digital hologram apparatus. Digital hologram data, determined from the sensed light, is dependent upon complex valued reflection coefficients of the object and upon phase perturbations in propagation paths between the object and the sensor array. Reflectance values, which may be dependent upon expected values of the absolute square of the reflection coefficients, and phase perturbations are determined for which a test function is at an extremum, where the test function contains a data fidelity term dependent upon the hologram data from a single hologram, a first regularization term dependent upon the phase perturbations and a second regularization term dependent upon the reflectance values. An image of the object may be formed from the reflectance values and a wavefront of the reflected light may be determined from the phase perturbations.

Abstract: Light reflected from an illuminated object is mixed with a reference beam and sensed at a sensor array of a digital hologram apparatus. Digital hologram data, determined from the sensed light, is dependent upon complex valued reflection coefficients of the object and upon phase perturbations in propagation paths between the object and the sensor array. Reflectance values, which may be dependent upon expected values of the absolute square of the reflection coefficients, and phase perturbations are determined for which a test function is at an extremum, where the test function contains a data fidelity term dependent upon the hologram data from a single hologram, a first regularization term dependent upon the phase perturbations and a second regularization term dependent upon the reflectance values. An image of the object may be formed from the reflectance values and a wavefront of the reflected light may be determined from the phase perturbations.

Abstract: An iterative reconstruction approach is provided that allows the use of differing weights in pixels or larger sub-regions in the reconstructed image. By way of example, the relative significance of each projection measurement may be determined based on both the measurement position and the location of the reconstructed pixel. Computationally, the significance of each projection based on these two factors is represented by a weight factor employed in the algorithmic computation.

Abstract: The present approach relates to the training of a machine learning algorithm for image generation and use of such a trained algorithm for image generation. Training the machine learning algorithm may involve using multiple images produced from a single set of tomographic projection or image data (such as a simple reconstruction and a computationally intensive reconstruction), where one image is the target image that exhibits the desired characteristics for the final result. The trained machine learning algorithm may be used to generate a final image corresponding to a computationally intensive algorithm from an input image generated using a less computationally intensive algorithm.

Abstract: A method for reconstructing an image of an object that includes a plurality of image elements. The method includes accessing image data associated with a plurality of image elements, and reconstructing an image of the object by optimizing an objective function, where the objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. The algorithm is composed of nested iterative loops, in which an inner loop iteratively optimizes an objective function approximating the outer loop objective function, and an outer loop that utilizes the solution of the inner loop to optimize the original objective function.

Abstract: A method for reconstructing image component densities of an object includes acquiring multi-spectral x-ray tomographic data, performing a material decomposition of the multi-spectral x-ray tomographic data to generate a plurality of material sinograms, and reconstructing a plurality of material component density images by iteratively optimizing a functional that includes a joint likelihood term of at least two of the material decomposed sinograms. An x-ray tomography imaging system and a non-transitory computer readable medium are also described herein.

Abstract: A method for reconstructing image component densities of an object includes acquiring multi-spectral x-ray tomographic data, performing a material decomposition of the multi-spectral x-ray tomographic data to generate a plurality of material sinograms, and reconstructing a plurality of material component density images by iteratively optimizing a functional that includes a joint likelihood term of at least two of the material decomposed sinograms. An x-ray tomography imaging system and a non-transitory computer readable medium are also described herein.

Abstract: A method for reconstructing an image of an object that includes a plurality of image elements. The method includes accessing image data associated with a plurality of image elements, and reconstructing an image of the object by optimizing an objective function, where the objective function is optimized by iteratively solving a nested sequence of approximate optimization problems. The algorithm is composed of nested iterative loops, in which an inner loop iteratively optimizes an objective function approximating the outer loop objective function, and an outer loop that utilizes the solution of the inner loop to optimize the original objective function.

Abstract: A method for creating from a digitized color image, a tree structured partitioning of all pixels in the image into M disjoint sets, with the pixels in each set having similar color values, the method comprising the steps of: separating and assigning all pixels in the image into disjoint sets so as to minimize the difference between the respective pixel color values in each set; selecting one of the sets which has a greatest variation of color values of assigned pixels, and separating and assigning all pixels in the selected set into new, disjoint sets, with the pixels assigned so as to minimize the differences between their respective color values; continuing the selecting, separating and assigning steps for sets until a total of M sets have been derived; and then assigning to all pixels in each set, a mean color value calculated from all pixel colors in the set.