Provided are systems and methods for image processing such as image modification. More particularly, example aspects of the present disclosure are directed to systems and methods for cascaded multi-resolution machine learning for performing image processing on resource-constrained devices.

Provided are systems and methods for image processing such as image modification. More particularly, example aspects of the present disclosure are directed to systems and methods for cascaded multi-resolution machine learning for performing image processing on resource-constrained devices.

An objective of an endoscope can be evaluated by collecting a series of differently focused images and digitally stitching them together to obtain a final image for the endoscope that can be then evaluated. Movable optics and/or a camera can be used to collect the series of differently focused images. Image processing algorithms can be used to evaluate the collected images in terms of image sharpness and identify the areas at which each image is in relatively good focus. Once the areas of good focus are identified, the image processing algorithms can extract the areas of good focus. The digital stitching algorithms can be used to assemble the extracted areas of good focus to form a final image where most of the target scene should be in focus. The final image is then reviewed to determine the acceptability of the objective.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for transferring global style features between digital images utilizing one or more machine learning models or neural networks. In particular, in one or more embodiments, the disclosed systems receive a request to transfer a global style from a source digital image to a target digital image, identify at least one target object within the target digital image, and transfer the global style from the source digital image to the target digital image while maintaining an object style of the at least one target object.

Region-of-interest (ROI)-based image enhancement using a residual network, including: generating, based on an input image and a residual path of a residual network, a first output corresponding to a region-of-interest of the input image; generating, based on the input image and a skip path of the residual network, a second output; and generating an output image based on the first output and the second output.

This application relates to a camera control method and apparatus, and a storage medium. The method is applied to a first terminal device, and the method includes: receiving image data from a second terminal device, where the image data is captured by the second terminal device in a photographing process; determining an operation command and status information, where the operation command is an operation command for the photographing process of the second terminal device, and the status information indicates an execution status of the operation command executed by the second terminal device; displaying a picture based on the image data; and displaying the execution status of the operation command on the picture based on the operation command and the status information.

A method includes determining depth values associated with a first set of pixel locations in a first image of a mouth. One or more function is generated for one or more color channels based on intensities of the one or more color channels at the first set of pixel locations and depth values associated with the first set of pixel locations. Image data comprising a new representation of the teeth is received, wherein the image data comprises a second set of pixel locations and new depth values associated with the second set of pixel locations. A new image is generated based on the image data and the one or more functions.

A non-transitory computer-readable medium storing a computer program executed by a computer, a method, and an image processing device are disclosed that are capable of compensating a missing region in a tomographic image in a state in which a part of a lumen organ is missing. In accordance with the program, a computer acquires a plurality of tomographic images of a cross section of the lumen organ captured at a plurality of places using a catheter. In addition, the computer extracts, from the plurality of tomographic images, a tomographic image in which the part of the lumen organ is missing. Then, the computer compensates a missing region of the lumen organ for the extracted tomographic image.

The technology of this application relates to a method for removing reflections from an image. The method detects one or more reflection areas in the image, wherein each reflection area includes a reflection. Further, the method extracts the one or more reflection areas from the image, and removes the reflection from each of the extracted reflection areas.

A method for enhancing a wide angle image to improve the perspectives and the visual appeal thereof wide-angle images uses custom adaptive dewarping. The method is based on the scene image content of recognized objects in the image, the position of these objects in the image, the depth of these objects in the scene with respect to other objects and the general context of the image.