Abstract: Systems and methods for training an implant plan evaluation model is provided. A first implant plan option having a first set of parameters and a second implant plan option having a second set of parameters may be received. The first set of parameters and the second set of parameters may be inputted into a model configured to score the first implant plan option based on the first set of parameters and the second implant plan option based on the second set of parameters. The score of the first implant plan option and the score of the second implant plan option may be compared. when the score of the second implant plan option is higher than the score of the first implant plan option the model may be adjusted to score the first implant plan option higher than the second implant plan option.

Abstract: Examples analyze a digital image to determine an amount of high-frequencies corresponding to each pixel of the digital image based at least in part on gradients corresponding to a pixel neighborhood of each pixel. Examples analyze the digital image to determine a degree of randomness for each pixel based at least in part on the corresponding gradients. Examples generate an activity mask for the digital image based at least in part on the amount of high-frequencies and the degree of randomness corresponding to each pixel. Examples process the pixels of the digital image by performing gray component replacement to generate a color transformed image based at least in part on the activity mask.

Abstract: Examples analyze a digital image to determine an amount of high-frequencies corresponding to each pixel of the digital image based at least in part on gradients corresponding to a pixel neighborhood of each pixel. Examples analyze the digital image to determine a degree of randomness for each pixel based at least in part on the corresponding gradients. Examples generate an activity mask for the digital image based at least in part on the amount of high-frequencies and the degree of randomness corresponding to each pixel. Examples process the pixels of the digital image by performing gray component replacement to generate a color transformed image based at least in part on the activity mask.

Abstract: Examples analyze a digital image to determine an amount of high-frequencies corresponding to each pixel of the digital image based at least in part on gradients corresponding to a pixel neighborhood of each pixel. Examples analyze the digital image to determine a degree of randomness for each pixel based at least in part on the corresponding gradients. Examples generate an activity mask for the digital image based at least in part on the amount of high-frequencies and the degree of randomness corresponding to each pixel. Examples process the pixels of the digital image by performing gray component replacement to generate a color transformed image based at least in part on the activity mask.

Abstract: Streaks in images are detected by determining a rotation angle of an image and calculating a rotated image based on a rotation of the image by the rotation angle. A cross-sectional pixel profile of the rotated image is determined and a peak in the cross-sectional pixel profile of the rotated image is determined. A streak in the image is identified based on the determined peak.

Abstract: An example system includes a histogram engine. The histogram engine is to generate a target histogram based on a target image. The example system also includes a dispersion engine to calculate a dispersion of the target histogram. The example system also includes a spreading engine to diffuse a reference histogram for a reference image based on the dispersion of the target histogram. The histogram engine also is to generate a mapping between the reference histogram with diffusion and the target histogram.

Abstract: Examples analyze a digital image to determine an amount of high-frequencies corresponding to each pixel of the digital image based at least in part on gradients corresponding to a pixel neighborhood of each pixel. Examples analyze the digital image to determine a degree of randomness for each pixel based at least in part on the corresponding gradients. Examples generate an activity mask for the digital image based at least in part on the amount of high-frequencies and the degree of randomness corresponding to each pixel. Examples process the pixels of the digital image by performing gray component replacement to generate a color transformed image based at least in part on the activity mask.

Abstract: Streaks in images are detected by determining a rotation angle of an image and calculating a rotated image based on a rotation of the image by the rotation angle. A cross-sectional pixel profile of the rotated image is determined and a peak in the cross-sectional pixel profile of the rotated image is determined. A streak in the image is identified based on the determined peak.

Abstract: A method of detecting Moire artefacts in a digital image comprises calculating at least one parameter relating to at least one feature of the digital image. The at least one parameter is compared to at least one corresponding threshold, wherein the at least one threshold is determined by a machine learning process, based on a set of example images and reference parameters for the set of example images. It is determined whether the digital image contains Moire artefacts based on the results of the comparing.

Abstract: A method to generate an image in which a plurality of offset vectors for portions of a first image are determined by comparing the similarity of the portions to portion of a second image.

Abstract: A method to generate an image in which a plurality of offset vectors for portions of a first image are determined by comparing the similarity of the portions to portion of a second image.