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: Noise preserving models and methods for resolution recovery of x-ray computed tomography (e.g., using a computerized tool) are enabled. For example, a system can comprise: a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory, wherein the computer executable components comprise: a pair generation component that generates a pair of images, the pair of images comprising an input image and a ground truth image, a training component that trains a machine learning based sharpening algorithm by approximately minimizing a loss function that determines an error between a sharpened image and the ground truth image, and a sharpening component that, using the sharpening algorithm, sharpens the input image to generate the sharpened image, wherein the sharpened image comprises a second noise that is similar in intensity to a first noise of the input image.

Abstract: Techniques are described that facilitate generating neural network (NNs) tailored to optimize specific properties of medical images using novel loss functions. According to an embodiment, a system is provided that comprises a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components comprise a training component that trains a NN to generate a modified version of computed tomography (CT) data comprising one or more optimized properties relative to the CT data using a loss function tailored to control learning adaptation of the NN based on error attributed to one or more defined components associated with the CT data, resulting in a trained NN, wherein the one or more defined components comprise at least one of a frequency component or a spatial feature component.

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: An image processing device which is capable of accurately detect pixels covered by cloud shadows and remove effects of the cloud shadows in an images are provided. The device includes: a cloud transmittance calculation unit that calculates transmittance of the one or more clouds in an input image, for each pixel; a cloud height estimation unit that determines estimation of a height from the ground to each cloud in the input image to detect position of corresponding one or more shadows; an attenuation factor estimation unit that calculates attenuation factors for the direct sun irradiance by applying an averaging filter to the cloud transmittance calculated; and a shadow removal unit that corrects pixels affected by the one or more shadows, based on a physical model of a cloud shadow formation by employing the attenuation factors calculated and the position, and outputs an image which includes the pixels corrected.

Abstract: An image processing device which is capable of accurately detects and corrects areas affected by clouds even if multiple types or layers of clouds present in images, is disclosed. The device includes: a cloud spectrum selection unit for selecting at least one spectrum for each of pixels from spectra of one or more clouds present in an input image; an endmember extraction unit for extracting spectra of one or more endmembers other than the one or more clouds from those of the input image; and an unmixing unit for deriving fractional abundances of the respective spectra of one or more endmembers and a selected spectrum of one of the one or more clouds for the each of pixels in the input image.

Abstract: An image processing device 500 for detecting and correcting areas affected by a cloud in an input image is provided. The image processing device 500 includes: endmember extraction unit 501 that extracts a set of spectra of one or more endmembers from the input image; cloud spectrum acquisition unit 502 that acquires one cloud spectrum in the input image; endmember selection unit 503 that compares the endmember spectra with the cloud spectrum, removes one or more spectra from the set of spectra of the endmember spectra, the one or more spectra being same as or similar to the one cloud spectrum, and outputs a resultant set of spectra as an authentic set of spectra; and an unmixing unit 504 that derives, for each pixel in the input image, one or more fractional abundances of the one or more endmembers and a fractional abundance of cloud in the authentic set of spectra, and detects one or more cloud pixels in the input image.

Abstract: An image processing device which is capable of accurately detect pixels covered by cloud shadows and remove effects of the cloud shadows in an images are provided. The device includes: a cloud transmittance calculation unit that calculates transmittance of the one or more clouds in an input image, for each pixel; a cloud height estimation unit that determines estimation of a height from the ground to each cloud in the input image to detect position of corresponding one or more shadows; an attenuation factor estimation unit that calculates attenuation factors for the direct sun irradiance by applying an averaging filter to the cloud transmittance calculated; and a shadow removal unit that corrects pixels affected by the one or more shadows, based on a physical model of a cloud shadow formation by employing the attenuation factors calculated and the position, and outputs an image which includes the pixels corrected.

Abstract: An image processing system includes a storage device, and a processor coupled to the storage device and configured to extract, from an input image, an aerosol spectrum across a plurality of wavelength bands of the input image. The storage device contains therein a database storing a plurality of predetermined aerosol spectra, and a plurality of corresponding values associated with an aerosol optical property at the plurality of wavelength bands. The processor is further configured to retrieve, from the database, values associated with the aerosol optical property at the plurality of wavelength bands, the retrieved values corresponding to the extracted aerosol spectrum. The processor is further configured to correct the input image, by using the retrieved values associated with the aerosol optical property at the plurality of wavelength bands, to generate a corrected image.

Abstract: An image processing device which is capable of accurately detects and corrects areas affected by clouds even if multiple types or layers of clouds present in images, is disclosed.

Abstract: An image processing system includes a storage device, and a processor coupled to the storage device and configured to extract, from an input image, an aerosol spectrum across a plurality of wavelength bands of the input image. The storage device contains therein a database storing a plurality of predetermined aerosol spectra, and a plurality of corresponding values associated with an aerosol optical property at the plurality of wavelength bands. The processor is further configured to retrieve, from the database, values associated with the aerosol optical property at the plurality of wavelength bands, the retrieved values corresponding to the extracted aerosol spectrum. The processor is further configured to correct the input image, by using the retrieved values associated with the aerosol optical property at the plurality of wavelength bands, to generate a corrected image.