The present disclosure relates to locally enhancing medical images. In accordance with certain embodiments, a method includes determining a boundary of a region of interest in a displayed medical image, overlaying the boundary on the displayed medical image, adjusting a position of a collimator of a medical imaging system based on the determined boundary, enhancing image quality of the region of interest, and displaying the enhanced region of interest within the boundary.

An apparatus comprising an interface, a structured light projector and a processor. The interface may receive pixel data. The structured light projector may generate a structured light pattern. The processor may process the pixel data arranged as video frames, perform operations using a convolutional neural network to determine a binarization result and an offset value and generate disparity and depth maps in response to the video frames, the structured light pattern, the binarization result, the offset value and a removal of error points. The convolutional neural network may perform a partial block summation to generate a convolution result, compare the convolution result to a speckle value to determine the offset value, generate an adaptive result in response to performing a convolution operation, compare the video frames to the adaptive result to generate the binarization result for the video frames, and remove the error points from the binarization result.

A method of image processing based on a plurality of frames of images, an electronic device, and a storage medium are provided. The method includes: capturing a plurality of frames of original images; obtaining a high dynamic range (HDR) image by performing image synthesis on the plurality of frames of original images; performing artificial intelligent-based denoising on the HDR image to obtain a target denoised image.

An image processing method and an image processing unit for performing image processing determines a set of one or more filtered pixel values, wherein the one or more filtered pixel values represent a result of processing image data using a set of one or more filtering functions. A total covariance of the set of one or more filtering functions is identified. A refinement filtering function is applied to the set of one or more filtered pixel values to determine a set of one or more refined pixel values, wherein the refinement filtering function has a covariance that is determined based on the total covariance of the set of one or more filtering functions.

The subject disclosure presents systems and methods for automatically selecting meaningful regions on a whole-slide image and performing quality control on the resulting collection of FOVs. Density maps may be generated quantifying the local density of detection results. The heat maps as well as combinations of maps (such as a local sum, ratio, etc.) may be provided as input into an automated FOV selection operation. The selection operation may select regions of each heat map that represent extreme and average representative regions, based on one or more rules. One or more rules may be defined in order to generate the list of candidate FOVs. The rules may generally be formulated such that FOVs chosen for quality control are the ones that require the most scrutiny and will benefit the most from an assessment by an expert observer.

A frequency adaptive descreening method includes obtaining a scan image of an original document, dividing a region of the scan image by analyzing frequency characteristics of the obtained scan image, estimating a resolution with respect to each of regions resulting from dividing the region according to the analyzed frequency characteristics, and adaptively performing filtering on the regions resulting from dividing the region by using the estimated resolution.

A system and method are provided for capturing an image with correct skin tone exposure. In use, one or more faces having threshold skin tone are detected within a scene. Based on the detected one or more faces, a high dynamic range (HDR) capture mode is enabled. Further, the scene image is captured using the HDR capture mode.

In one embodiment, a vision aided navigation system comprises: at least one image sensor configured to produce image frames of a surrounding environment; a feature extractor configured to extract at least one image feature from a first image frame; a navigation filter configured to output a navigation solution based navigation data from a navigation device and changes in position of the image feature in the images; a feature tracker to receive the image frames and predict a location of the image feature in a subsequent image frame; a dynamic localized parameter adjuster to adjust at least one image parameter of the subsequent image frame; and wherein the feature tracker is configured so that when the image feature cannot be identified in the subsequent image frame within a bounded region around the predicted location, the dynamic localized parameter adjuster adjusts the at least one image parameter within the bounded region.

A system includes an image sensor, an imaging pipeline, and a display device. The image sensor is configured to capture a first frame of pixel data. The imaging pipeline is coupled to the image sensor to receive the first frame of pixel data. The imaging pipeline includes an adaptive noise filter. The adaptive noise filter is configured to filter a pixel based on noise in the pixel. The imaging pipeline is configured to output a second frame of pixel data. The second frame of pixel data includes pixels filtered by the adaptive noise filter. The display device is coupled to the imaging pipeline to receive the second frame of pixel data. The display device is configured to display the second frame of pixel data.

A method of generating a high dynamic range (HDR) image is provided that includes capturing a long exposure image and a short exposure image of a scene, computing a merging weight for each pixel location of the long exposure image based on a pixel value of the pixel location and a saturation threshold, and computing a pixel value for each pixel location of the HDR image as a weighted sum of corresponding pixel values in the long exposure image and the short exposure image, wherein a weight applied to a pixel value of the pixel location of the short exposure image and a weight applied to a pixel value of the pixel location in the pixel long exposure image are determined based on the merging weight computed for the pixel location and responsive to motion in a scene of the long exposure image and the short exposure image.