Abstract: Devices, systems, and methods obtain an image; perform at least one nonlinear locally-adaptive mapping on the image, thereby generating a revised image; generate an edge-enhanced image based on the revised image; and perform dynamic contrast stretching on the edge-enhanced image, thereby generating an enhanced image.

Abstract: Some embodiments of devices, systems, and methods generate a respective connection mask at each defect detection location in a binary defect map, wherein each detection location in the binary defect map has a value that indicates that the detection location either has a defect or, alternatively, does not have a defect; generate one or more clusters of defect detection locations in the binary defect map based on each defect location's respective connection mask and on the binary defect map; generate respective bounding boxes for the one or more clusters; and recursively perform the following: checking respective sizes of the bounding boxes, and splitting any clusters of the one or more clusters that have respective bounding boxes with sizes that exceed one or more thresholds, thereby generating additional clusters of defect locations.

Abstract: An apparatus for automatically adjusting a collection of images based on their lighting conditions is provided. The apparatus obtains one or more images, determines a first lighting condition scores for each lighting condition and for each of the one or more images using a trained prediction model, and labels the each of the one or more images based on the determined first lighting condition scores.

Abstract: Some embodiments of systems, devices, and methods obtain a plurality of training images; obtain one or more target regions; generate one or more respective statistical characterizations of the one or more target regions; obtain a test image; locate the one or more target regions on the test image; compare the one or more target regions in the test image to the one or more respective statistical characterizations of the one or more target regions; and detect anomalies in the one or more target regions in the test image based at least in part on the comparison of the one or more target regions in the test image to the one or more respective statistical characterizations of the one or more target regions.

Abstract: A communication apparatus automatically establishes a wireless communication connection with an image processing apparatus. The communication apparatus captures one of more images of the image processing apparatus and determines, via image recognition process the identification information associated with the image processing apparatus. The communication apparatus automatically searches configuration information matching the identification information of the image processing apparatus and initiates the communication connection request. Communication apparatus provides the status information on a display screen of the communication apparatus and establishes a connection between the image processing apparatus and the communication apparatus.

Abstract: Some embodiments of systems, devices, and methods obtain a plurality of training images; obtain one or more target regions; generate one or more respective statistical characterizations of the one or more target regions; obtain a test image; locate the one or more target regions on the test image; compare the one or more target regions in the test image to the one or more respective statistical characterizations of the one or more target regions; and detect anomalies in the one or more target regions in the test image based at least in part on the comparison of the one or more target regions in the test image to the one or more respective statistical characterizations of the one or more target regions.

Abstract: Some embodiments of devices, systems, and methods generate a respective connection mask at each defect detection location in a binary defect map, wherein each detection location in the binary defect map has a value that indicates that the detection location either has a defect or, alternatively, does not have a defect; generate one or more clusters of defect detection locations in the binary defect map based on each defect location's respective connection mask and on the binary defect map; generate respective bounding boxes for the one or more clusters; and recursively perform the following: checking respective sizes of the bounding boxes, and spitting any clusters of the one or more clusters that have respective bounding boxes with sizes that exceed one or more thresholds, thereby generating additional clusters of defect locations.

Abstract: A communication apparatus automatically establishes a wireless communication connection with an image processing apparatus. The communication apparatus captures one of more images of the image processing apparatus and determines, via image recognition process the identification information associated with the image processing apparatus. The communication apparatus automatically searches configuration information matching the identification information of the image processing apparatus and initiates the communication connection request. Communication apparatus provides the status information on a display screen of the communication apparatus and establishes a connection between the image processing apparatus and the communication apparatus.

Abstract: Devices, systems, and methods obtain an image; perform at least one nonlinear locally-adaptive mapping on the image, thereby generating a revised image; generate an edge-enhanced image based on the revised image; and perform dynamic contrast stretching on the edge-enhanced image, thereby generating an enhanced image.

Abstract: A method for reducing noise in a medical diagnostic image includes acquiring an initial three-dimensional (3D) volume of projection data, generating a projection space noise estimate using the 3D volume of projection data, generating an initial 3D volume of image data using the 3D volume of projection data, generating an image space noise estimate using the 3D volume of image data, generating a noise projection estimate using the projection space noise estimate and the image space noise estimate, and reconstructing an image using the generated noise estimate. A system and non-transitory computer readable medium are also described.

Abstract: A method for reducing noise in a medical diagnostic image includes acquiring an initial three-dimensional (3D) volume of projection data, generating a projection space noise estimate using the 3D volume of projection data, generating an initial 3D volume of image data using the 3D volume of projection data, generating an image space noise estimate using the 3D volume of image data, generating a noise projection estimate using the projection space noise estimate and the image space noise estimate, and reconstructing an image using the generated noise estimate. A system and non-transitory computer readable medium are also described.