A system and a method for monitoring a brain CT scan image using ASPECTS score. The method includes receiving the brain CT scan image of a patient. Further, a basal ganglia region and a corona radiata level are identified in a plurality of slices in the brain CT scan image. Furthermore, a plurality of anatomical regions, a plurality of infarcts and a plurality of black regions are segmented using deep learning. Subsequently, an overlapping region across the plurality of slices is determined based on the plurality of anatomical regions, the plurality of infarcts, and the plurality of black regions. The overlapping region and a predefined threshold are used to compute an ASPECTS score. The ASPECTS score is further used to recommend a course of action to the patient.

A system for low compute high-resolution depth map generation using low-resolution cameras is configured to obtain a stereo pair of images and generate a depth map by performing stereo matching on the stereo pair of images. The system is also configured to obtain a first image comprising first texture information for the environment that has a first image resolution that is higher than an image resolution of images of the stereo pair of images. The system is further configured to generate a reprojected first image by reprojecting the first image to correspond to an image capture perspective associated with the depth map. The reprojection of the first image is based on depth information from the depth map and includes reprojected first texture information for the environment. The system is also configured to generate an upsampled depth map based on the depth map.

A method for removing haziness in a digital image is provided. The method includes receiving an input digital image having some haze content from an image capturing device. The input digital image is downscaled to obtain a low-resolution image. Further, a minimum intensity dark channel is determined for each local patch of the low-resolution image to obtain a dark channel image corresponding to the low-resolution image. Furthermore, a transmission map of the low-resolution image is determined based on the dark channel image. Moreover, an atmospheric light value associated with the low-resolution image is also determined. The method further includes applying the determined transmission map and the atmospheric light value associated with the low-resolution image to the input digital image to generate a de-hazed output image and displaying the generated de-hazed output image on a display unit.

A method comprising: receiving an image of a scene, segmenting the image into a plurality of segments, obtaining at least one depth sample from each of at least some of the segments, and with respect to each of the at least some of the segments, assigning a value of the depth sample to each pixel in the segment, to create a depth image of the image.

A system and a method for detecting welding based on edge computing, the system includes at least one edge server, each edge server configured to obtain welding information of at least one welding machine, preprocess the welding information to generate processed data, input the processed data to a trained algorithm to generate a detecting result, and determine a welding quality of the welding machine according to the detecting result; and a data server coupled to the at least one edge server and configured to process and store the detecting result and the welding information uploaded by each edge server, generate display information, and visualizes the detecting result according to the display information.

Skin smoothing is applied to images using a bilateral filter and aided by a skin map. In one example a method includes receiving an image having pixels at an original resolution. The image is buffered. The image is downscaled from the original resolution to a lower resolution. A bilateral filter is applied to pixels of the downscaled image. The filtered pixels of the downscaled image are blended with pixels of the image having the original resolution, and the blended image is produced.

According to embodiments of the present disclosure, a method and an apparatus for processing an image, a device, and a storage medium are provided. The method includes: performing an image processing operation on an initial image having a noise associated with an adversarial sample attack, to obtain an intermediate image, the image processing operation including at least one of: reducing resolution of the initial image, or smoothing at least a part of the initial image; determining an image enhancement model matching the image processing operation, the image enhancement model being trained based on a sample image and a reference image, and the reference image being obtained by performing at least the image processing operation on the sample image; and generating a target image by processing the intermediate image using the image enhancement model, the target image having an image quality higher than the intermediate image.

A system for low compute high-resolution depth map generation using low-resolution cameras is configured to obtain a stereo pair of images and generate a depth map by performing stereo matching on the stereo pair of images. The system is also configured to obtain a first image comprising first texture information for the environment that has a first image resolution that is higher than an image resolution of images of the stereo pair of images. The system is further configured to generate a reprojected first image by reprojecting the first image to correspond to an image capture perspective associated with the depth map. The reprojection of the first image is based on depth information from the depth map and includes reprojected first texture information for the environment. The system is also configured to generate an upsampled depth map based on the depth map.

A high-speed and high-accuracy spectral video system has a filter module that filters optical signals in desired bands; a beam splitting module that splits the signal from the filter module into two identical beams entering an encoding aperture module and an RGB information acquisition module, respectively; a dispersion module disperses the optical signal and transmits the dispersed signal to a grayscale information acquisition module; a data reconstruction module aligns the signal from the grayscale information acquisition module to the signal from the RGB information acquisition module, denoises the signals, reconstructs a video by a bilateral filtering algorithm, and sends the reconstructed video to a display module for storage and display. A flame spectrum can be reconstructed using few sampling points to obtain broad-band spectral characteristics of the flame or using many sampling points to obtain high-accuracy spectral data.

Image data obtained by imaging of an imaging element capable of imaging a subject with sensitivity to a wavelength band of visible light and a wavelength band of near-infrared light via an optical system is acquired. A point image restoration process using a common restoration filter is performed on the image data of the subject captured with sensitivity to the wavelength band of the visible light by the imaging element and the image data of the subject captured with sensitivity to the wavelength band of the near-infrared light by the imaging element. The common restoration filter is calculated on the basis of average optical characteristics of the optical system obtained by performing weighted averaging of first optical characteristics with respect to the visible light of the optical system and second optical characteristics with respect to the near-infrared light of the optical system.