Disclosed herein are various techniques for more precisely and reliably (a) positioning top and bottom border edges relative to textual content, (b) positioning left and right border edges relative to textual content, (c) positioning mixed edge borders relative to textual content, (d) positioning boundaries of a region of background shading that fall within borders of textual content, (e) positioning borders relative to textual content that spans columns, (f) positioning respective borders relative to discrete portions of textual content, (g) positioning collective borders relative to discrete, abutting portions of textual content, (h) applying stylized corner boundaries to a region of background shading, and (i) applying stylized corners to borders.

A system, method, and computer program product are provided for obtaining low-noise, high-speed captures of a photographic scene. In use, a first cell of a first pixel is in communication with a first node for storing a first sample. Further, a second cell of a second pixel is in communication with a second node for storing a second sample. Still further, the first cell and the second cell are communicatively coupled.

Synthesis of an image of a view from data of a multi-view video. The synthesis includes an image processing phase as follows: generating image synthesis data from texture data of at least one image of a view of the multi-view video; calculating an image of a synthesised view from the generated synthesis data and at least one image of a view of the multi-view video; analysing the image of the synthesised view relative to a synthesis performance criterion; if the criterion is met, delivering the image of the synthesised view; and if not, iterating the processing phase. The calculation of an image of a synthesised view at a current iteration includes modifying, based on synthesis data generated in the current iteration, an image of the synthesised view calculated during a processing phase preceding the current iteration.

A method and apparatus for processing an image signal, an electronic device, and a computer-readable storage medium. The method includes: obtaining a digital image signal of a target image, the target image including object imaging corresponding to an object, identifying a first area of the object imaging in the target image from the digital image signal, removing the object imaging from the target image based on the first area, to obtain a background image corresponding to an original background, performing image inpainting processing on the first area of the background image to obtain a filled image, the filled image including the original background and a perspective background connected to the original background, identifying a second area in the object imaging, and removing an imaging portion corresponding to the second area from the object imaging, and superimposing the obtained adjusted object imaging on the first area.

A method of fusing an image, a method of training an image fusion model, an electronic device, and a storage medium. The method of fusing the image includes: encoding a stitched image obtained by stitching a foreground image and a background image, so as to obtain a feature map; and decoding the feature map to obtain a fused image, wherein the feature map is decoded by: performing a weighting on the feature map by using an attention mechanism, so as to obtain a weighted feature map; performing a fusion on the feature map according to feature statistical data of the weighted feature map, so as to obtain a fused feature; and decoding the fused feature to obtain the fused image.

Systems and methods for image processing for determining a registration map between a first image of a scene with a second image of the scene, include solving an optimal transport (OT) problem to produce the registration map by optimizing a cost function that determines a minimum of a ground cost distance between the first and the second images modified with an epipolar geometry-based regularizer including a distance that quantifies the violation of an epipolar geometry constraint between corresponding points defined by the registration map. The ground cost compares a ground cost distance of features extracted within the first image with a ground cost distance of features extracted from the second image.

An image positioning system based on upsampling and a method thereof are provided. The image positioning method based on upsampling is to fetch a region image covering a target from a wide region image, determine a rough position of the target, execute an upsampling process on the region image based on neural network data model for obtaining a super-resolution region image, map the rough position onto the super-resolution region image, and analyze the super-resolution region image for determining a precise position of the target. The present disclosed example can significantly improve the efficiency of positioning and effectively reduce the required cost of hardware.

Systems and methods for eye image set selection, eye image collection, and eye image combination are described. Embodiments of the systems and methods for eye image set selection can include comparing a determined image quality metric with an image quality threshold to identify an eye image passing an image quality threshold, and selecting, from a plurality of eye images, a set of eye images that passes the image quality threshold.

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 fusion system provides a predicted alignment between images of different modalities and synchronization of the alignment, once acquired. A spatial tracker detects and tracks a position and orientation of an imaging device within an environment. A predicted pose of an anatomical feature can be determined, based on previously acquired image data, with respect to a desired position and orientation of the imaging device. When the imaging device is moved into the desired position and orientation, a relationship is established between the pose of the anatomical feature in the image data and the pose of the anatomical feature imaged by the imaging device. Based on tracking information provided by the spatial tracker, the relationship is maintained even when the imaging device moves to various positions during a procedure.