A method for applying adaptive zoom on an image is disclosed. The method includes detecting at least one input to perform zoom on the image, determining key pixels in the image, obtaining a plurality of segments by determining sizes of each of the plurality of the segments based on a number of the key pixels in each of the plurality of the segments, determining a priority order for zooming the plurality of the segments based on a density of the key pixels in each of the plurality of the segments, determining a zoom level to be applied on each of the plurality of the segments based on the density of the key pixels in each of the plurality of the segments, and adaptively zooming each of the plurality of the segments based on the zoom level and the priority order.

An image processing apparatus includes: a memory storing one or more instructions; and a processor configured to execute the one or more instructions to: obtain similarity information, based on a pixel difference between each pixel included in a first image and an adjacent pixel of each pixel included in the first image; determine weight information based on the similarity information; obtain first feature information by performing a convolution operation between the first image and a first kernel; obtain second feature information by applying the weight information to the first feature information; and generate a second image based on the second feature information.

A system and method are provided for capturing an image with correct skin tone exposure. In use, one or more faces are detected having threshold skin tone within a scene. Next, based on the detected one or more faces, the scene is segmented into one or more face regions and one or more non-face regions. A model of the one or more faces is constructed based on a depth map and a texture map, the depth map including spatial data of the one or more faces, and the texture map includes surface characteristics of the one or more faces. The one or more images of the scene are captured based on the model. Further, in response to the capture, the one or more face regions are processed to generate a final image.

Systems and methods for performing motion artifact correction in medical images. One method includes receiving, with an electronic processor, a medical image associated with a patient, the medical image including at least one motion artifact. The method also includes applying, with the electronic processor, a model developed using machine learning to the medical image for correcting motion artifacts, the model including at least one of a spatial transformer network and an attention mechanism network. The method also includes generating, with the electronic processor, a new version of the medical image, where the new version of the medical image at least partially corrects the at least one motion artifact.

An image filtering arrangement comprising a controller configured to receive an image data file; propose zero or more regions of interest for the image data file; and to select adaptive filtering for at least one of the proposed zero or more regions of interest and apply the selected adaptive filtering to the at least one of the proposed zero or more regions of interest.

Methods for automated segmentation system for retinal blood vessels from optical coherence tomography angiography images include a preprocessing stage, an initial segmentation stage, and a refining stage. Application of machine-learning techniques to segmented images allow for automated diagnosis of retinovascular diseases, such as diabetic retinopathy.

The invention relates to a method for determining errors in at least one parameter of the object derived from a digital representation of an object, wherein the digital representation comprises a large number of pixels arranged on a grid. At least one item of image information that quantifies a material-specific value of the object at the position of the pixel is assigned to a pixel. The image information results from a metrological mapping of the object, and is overlaid with statistical noise. As a result of the metrological mapping of the object, the image information of a first pixel is correlated to the image information of pixels within a surroundings of the first pixel defined by a correlation length of the image.

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.

Systems and techniques are described for image processing. An imaging system can include an image sensor that captures image data. An image signal processor (ISP) of the imaging system can demosaic the image data. The imaging system can input the image data into one or more trained machine learning models, in some cases along with metadata associated with the image data. The one or more trained machine learning models can output settings for a set of parameters of the ISP based on the image data and/or the metadata. The imaging system can generate an output image by processing the image data using the ISP, with the parameters of the ISP set according to the settings. Each pixel of the pixels of the image data can be processed using a respective setting for adjusting a corresponding parameter. The parameters of the ISP can include gain, offset, gamma, and Gaussian filtering.

An image processing method includes acquiring a captured image obtained by imaging, generating a first image by correcting a blur component of the captured image, and generating a second image based on the captured image, the first image, and weight information. The weight information is generated based on (i) information on brightness of the captured image or information on a scene of the captured image and (ii) information on a saturated area in the captured image.