The disclosure provides a model generation method based on a multi-view panoramic image, including: calculating an image rectification rotation matrix of source images and a reference image; extracting a reference image feature of the reference image and source image features of the source images; performing a fusion operation on rectified cost volumes of the plurality of source images corresponding to the reference image to obtain a final cost volume; calculating an estimated phase difference under a set resolution; obtaining a final phase difference of the reference image; and generating a depth map of the reference image, and constructing a corresponding stereo vision model.

A method for generating a color-faithful extended-depth-of-field (EDF) image from a color volume of 2D images acquired at different focal depths using a microscope. The method involves: generating a grayscale volume; applying invertible color-to-grayscale transformation to the volume; applying wavelet transform to the grayscale volume to obtain a 3D wavelet-coefficient-matrix (WCM); selecting wavelet coefficients using a coefficient selection rule; generating a 2D-WCM and a 2D coefficient-map (CM); applying inverse transformation of the wavelet transform to the 2D-WCM to obtain a 2D grayscale EDF image; generating a 2D color-composite(CC) image; applying inverse transformation of the color-to-grayscale transformation to the 2D grayscale EDF image to obtain a 2D color EDF image; converting the 2D-CC image and the 2D color EDF image into a color space including chromaticity and intensity component(s); and concatenating, chromaticity component(s) of the 2D-CC image and intensity component(s) of the 2D color EDF image, to obtain a color-faithful EDF image.

The present disclosure generally pertains to a time-of-flight imaging circuitry configured to: obtain first image data from an image sensor, the first image data being indicative of a scene, which is illuminated with spotted light; determine a first image feature in the first image data; obtain second image data from the image sensor, the second image data being indicative of the scene; determine second image feature in the second image data; estimate a motion of the second image feature with respect to the first image feature; and merge the first and the second image data based on the estimated motion.

A method for microscope-based super-resolution includes acquiring a to-be-processed image and at least an auxiliary image, the to-be-processed image includes a target area, the auxiliary image includes an overlapping portion with the target area, and the to-be-processed image and the auxiliary image are both microscope images of a first resolution. The method further includes registering the to-be-processed image and the auxiliary image to obtain a registered image, and extracting one or more high-resolution features from the registered image. The one or more high-resolution features represent image features of the target area in a second resolution, and the second resolution is greater than the first resolution. The method also includes reconstructing, based on the one or more high-resolution features, a target image of the second resolution corresponding to the to-be-processed image of the first resolution. Apparatus and non-transitory computer-readable storage medium counterpart embodiments are also contemplated.

This disclosure relates to image processing method and apparatus. The method includes: processing, with a first generator in an image processing model, a first sample image in a first sample set to obtain a first predicted image; processing, with the first generator, a second sample image in a second sample set to obtain a second predicted image; and training the image processing model according to a difference between the target avatar in the first sample image and the first predicted avatar and a difference between a first type attribute of the sample avatar in the second sample image and a first type attribute of the second predicted avatar.

The disclosure discloses a mobile photoelectric detection and identification system for low, slow and small targets. The optical detection subsystem and the photoelectric parallel processing and identification subsystem are arranged on the servo subsystem, and the servo subsystem is carried on an installation platform of a vehicle. The optical detection subsystem is configured to collect multi-wavelength band optical information from the target and the background. The co-processing module of various wavelength bands is configured to perform single-frame detection and identification of the target from the image information of the corresponding wavelength band. The information processing main control module is configured to use JPEG image compression, track association and multi-frame combining methods to perform a multi-frame detection and identification on the target. The servo subsystem is configured to complete target tracking according to the multi-frame detection and identification results.

An electronic apparatus may include: a video processor configured to output a video frame; a graphic processor configured to output a graphic frame; a mixer; and a processor which may be configured to: control the mixer to generate and output a first composite frame based on the video frame and the graphic frame, generate a second composite frame, which comprises a video area corresponding to the video frame and a graphic area corresponding to the graphic frame in a displayed image, and in which the video area and the graphic area have undergone image effect processing, based on an event of the image effect processing, and control the mixer to output the second composite frame.

Described herein is an image processing apparatus (701) comprising one or more processors (704) configured to: receive (601) a plurality of input images (301, 302, 303); for each input image, form (602) a set of decomposed data by decomposing the input image (301, 302, 303) or a filtered version thereof (307, 308, 309) into a plurality of frequency-specific components (313) each representing the occurrence of features of a respective frequency interval in the input image or the filtered version thereof; process (603) each set of decomposed data using one or more convolutional neural networks to form a combined image dataset (327); and subject (604) the combined image dataset (327) to a construction operation that is adapted for image construction from a plurality of frequency-specific components to thereby form an output image (333) representing a combination of the input images. The resulting HDR output image may have fewer artifacts and provide a better quality result. The apparatus is also computationally efficient, having a good balance between accuracy and efficiency.

An example electronic device may include a memory, an image sensor, and at least one processor operatively connected to the memory and the image sensor, wherein the memory is configured to store instructions which, when executed by the at least one processor, cause the electronic device to acquire, through the image sensor, a plurality of images including first images having a first size and at least one second image having a second size larger than the first size, acquire a first synthesis image based on the first images, acquire a first moving object portion from the first synthesis image, based on synthesis area information including at least one of moving object location information or background location information, identify a second moving object portion in the at least one second image, based on the synthesis area information, and acquire a second synthesis image by replacing the second moving object portion by the first moving object portion, and other embodiments are possible.

Techniques for aligning images generated by an integrated camera physically mounted to an HMD with images generated by a detached camera physically unmounted from the HMD are disclosed. A 3D feature map is generated and shared with the detached camera. Both the integrated camera and the detached camera use the 3D feature map to relocalize themselves and to determine their respective 6 DOF poses. The HMD receives the detached camera's image of the environment and the 6 DOF pose of the detached camera. A depth map of the environment is accessed. An overlaid image is generated by reprojecting a perspective of the detached camera's image to align with a perspective of the integrated camera and by overlaying the reprojected detached camera's image onto the integrated camera's image.