The present disclosure relates to a method for generating a view for a camera system, in particular a surround-view camera system for a vehicle, including a control device and at least one camera, wherein the view is generated by means of the following method steps: capturing at least one object from the environment data from the at least one camera; generating a bounding box for the object; projecting the object onto a ground plane; creating a bounding shape which includes the bounding box and the projected object; creating a mesh structure or grid structure for the bounding shape; and arranging the mesh structure or grid structure within the bounding box, wherein the bounding shape is adapted, in particular by image scaling and/or image distortion, to the size of the bounding box.

A head-mounted device may have a head-mounted housing. Optical components may be supported by the head-mounted housing. The optical components may include cameras such as front-facing cameras and/or movable optical modules that have displays for displaying images to eye boxes. Sensors may be provided in the head-mounted device to detect changes in orientation between respective optical modules, between respective portions of a chassis, display cover layer, or other head-mounted support structure in the housing, between optical components such as cameras, and/or between optical components and housing structures. Information from these sensors can be used to measure image misalignment such as image misalignment associated with misaligned cameras or misalignment between optical module images and corresponding eye boxes.

Embodiments relate to lateral chromatic aberration (LCA) recovery of raw image data generated by image sensors. A chromatic aberration recovery circuit performs chromatic aberration recovery on the raw image data to correct the resulting LCA in the full color images using pre-calculated offset values of a subset of colors of pixels.

A method for correcting a distorted image includes: acquiring a first coordinate of each pixel in a distorted image to be corrected; determining internal parameters for shooting the distorted image; acquiring a second coordinate corresponding to the first coordinate based on a corresponding relationship between the internal parameters and image distortion degrees, in which the second coordinate is an undistorted coordinate; acquiring a distance between the first coordinate and a coordinate of a center point of the distorted image, and determining a smoothing processing coefficient corresponding to the distance based on a smoothing processing function, in which the smoothing processing function is configured to indicate a proportional relationship between the distance and the smoothing processing coefficient; and acquiring a distortion correction image by performing smoothing correction on each first coordinate based on the smoothing processing coefficient and the second coordinate.

A camera parameter estimation apparatus 10 for estimating geometric parameters of a camera that has shot an image of an object and a lens distortion parameter of a lens distortion model represented by a single unknown. The camera parameter estimation apparatus 10 includes: a data obtaining unit 11 that obtains image corresponding points relating to the object and an approximation order for polynomial approximation of the lens distortion model; and a parameter estimation unit 12 that estimates, based on the image corresponding points and the approximation order, the geometric parameter and the lens distortion parameter that minimize an error function representing a specific transformation of the image corresponding points.

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 image processing apparatus includes a processor. The processor is configured to: acquire a first image, which is obtained by performing first AI processing on a processing target image, and a second image, which is obtained without performing the first AI processing on the processing target image; and adjust excess and deficiency of the first AI processing by combining the first image and the second image.

A method of distortion correction in an image captured by a non-rectilinear camera includes obtaining multiple images of a scene captured by the camera over time, determining where bottom portions of objects having moved over a horizontal surface in the scene are located in the images, determining a boundary of the horizontal surface in the scene based on the determined locations of the bottom portions, generating a three-dimensional model of the scene by defining one or more vertical surfaces around the determined boundary of the horizontal surface of the scene, and correcting a distortion of at least one of the images by projecting the image onto the three-dimensional model of the scene. A corresponding device, computer program and computer program product are also provided.

A camera monitoring system includes an imaging unit arranged in a moving object, and having a low-resolution area corresponding to an angle of view that is smaller than a predetermined angle of view and a high-resolution area corresponding to an angle of view that is larger than or equal to the predetermined angle of view and having a resolution higher than a resolution of the low-resolution area, a generating unit configured to generate a first video image including the high-resolution area based on a captured video image and a second video image including a video image captured with the low-resolution area, a processing unit configured to perform distortion correction processing to correct distortion of the second video image, a first display unit configured to display the first video image, and a second display unit configured to display the second video image.

A driver assistance system for a vehicle, the driver assistance system comprising a camera mounted on the vehicle and configured to capture one or more images inside and/or outside of the vehicle, and a processing unit, wherein the camera has a defined field of view FOV, the camera comprises an optical lens that causes a distortion of the captured images, the processing unit is configured to perform distortion compensation on the images captured by the camera, wherein a pixel density in the resulting compensated images is increased in defined areas of the image due to the distortion, and the defined areas of increased pixel density in the compensated images correspond to a region of interest ROI within the image, wherein the region of interest ROI is smaller than the field of view FOV of the camera.