A method for reconstructing a downsampled depth map includes receiving, at an electronic device, image data to be presented on a display of the electronic device at a first resolution, wherein the image data includes a color image and the downsampled depth map associated with the color image. The method further includes generating a high resolution depth map by calculating, for each point making up the first resolution, a depth value based on a normalized pose difference across a neighborhood of points for the point, a normalized color texture difference across the neighborhood of points for the point, and a normalized spatial difference across the neighborhood of points. Still further, the method includes outputting, on the display, a reprojected image at the first resolution based on the color image and the high resolution depth map. The downsampled depth map is at a resolution less than the first resolution.

A method for generating a super-resolution image and related device is provided. In one aspect, the method comprises: receiving a first low-resolution image and a second low-resolution image, the first low-resolution image and second low-resolution image have a first spatial resolution and having been captured simultaneously by a pair of pixel arrays of a common image sensor, wherein the pixel arrays of the image sensor are located as to be diagonally shifted from each other by a sub-pixel increment; adaptively enhancing the first low-resolution and the second low-resolution images to generate an enhanced first low-resolution image and an enhanced second low-resolution image, respectively; mapping (e.g., non-uniformly) pixels of each of the enhanced first and second low-resolution images to a super-resolution grid having a spatial resolution greater than the first spatial resolution to generate a first intermediate super-resolution image and a second intermediate super-resolution image, respectively; and combining the first intermediate super-resolution image and second intermediate super-resolution image to generate a composite super-resolution image.

A camera system captures an image in a source aspect ratio and applies a transformation to the input image to scale and warp the input image to generate an output image having a target aspect ratio different than the source aspect ratio. The output image has the same field of view as the input image, maintains image resolution, and limits distortion to levels that do not substantially affect the viewing experience. In one embodiment, the output image is non-linearly warped relative to the input image such that a distortion in the output image relative to the input image is greater in a corner region of the output image than a center region of the output image.

An electronic apparatus performs a method of real time simulation of physical visual effect on one or more Graphics Processing Units (GPUs). The method includes a plurality of time steps. Each of the time steps includes: building up a mapping between particles and background grid blocks; sorting the particles to a level of granularity; transferring momenta and masses of the particles to grid nodes on the background grid blocks to compute forces on the grid nodes; updating velocities and resolving collisions from the computed forces on the grid nodes; and applying the updated velocities back to the particles from the grid nodes and advecting the particles. In some embodiments, the frequency of building up and sorting is reduced compared with the frequency of transferring, updating, and applying in the plurality of time steps.

Disclosed are apparatuses, systems, and techniques that may perform methods of pyramid optical flow processing with efficient identification and handling of object boundary pixels. In pyramid optical flow, motion vectors for pixels of image layers having a coarse resolution may be used as hints for identification of motion vectors for pixels of image layers having a higher resolution. Pixels that are located near apparent boundaries between foreground and background objects may receive multiple hints from lower-resolution image layers, for more accurate identification of matching pixels across different image levels of the pyramid.

Embodiments relate to generating keypoint descriptors of the keypoints using a sub-scale refinement and a sample pattern radius adjustment. An apparatus includes a sub-pixel refiner circuit and a keypoint descriptor generator circuit. The sub-pixel refiner circuit determines a keypoint scale value for a scale dimension of a keypoint in an image pyramid by performing an interpolation of response map (RM) pixel values of a pixel block of RM images defined around the keypoint. The keypoint descriptor generator circuit determines sample scales of the image pyramid based on the keypoint scale value and determines a radius value for each sample scale based on the keypoint scale value. The keypoint descriptor generator circuit samples patches of pixel values at the sample scales using the radius value for each sample scale to generate a keypoint descriptor of the keypoint.

In an X-ray imaging apparatus, an image processor is configured to generate a super-resolved image having higher resolution in an X direction than a first fluoroscopic X-ray image and a second fluoroscopic X-ray image by dividing, in the X direction, a pixel value of a first pixel in the first fluoroscopic X-ray image based on pixel values of two pixels in the second fluoroscopic X-ray image that overlap the first pixel when the first fluoroscopic X-ray image and the second fluoroscopic X-ray image are shifted in the X direction by an amount corresponding to a movement amount (of an X-ray detection position) and displayed in an overlapping manner.

The technology disclosed relates to structured illumination microscopy (SIM). In particular, the technology disclosed relates to capturing and processing, in real time, numerous image tiles across a large image plane, dividing them into subtiles, efficiently processing the subtiles, and producing enhanced resolution images from the subtiles. The enhanced resolution images can be combined into enhanced images and can be used in subsequent analysis steps.

An image processing device comprises: one or more processors comprising hardware, the one or more processors being configured to: generate a high-resolution combined image by aligning a plurality of time-series images with each other in a high-resolution image space having a resolution higher than the plurality of time-series images based on an amount of displacement between the plurality of time-series images, and combining the plurality of time-series images; generate a low-resolution combined image by aligning the plurality of time-series images with each other in a low-resolution image space having a resolution equal to or lower than the resolution of the plurality of time-series images based on the amount of displacement, combining the plurality of time-series images through weighted addition; calculate a feature quantity pertaining to a pixel-value change direction at each region in the generated low-resolution combined image; and correct the high-resolution combined image based on the calculated feature quantity.

A computer-implemented method for increasing the image resolution of a digital image is provided. The method includes performing bicubic upsampling of the digital image to generate a base high-resolution (HR) image. The digital image is converted from a red-green-blue (RGB) color space to a Luma (Y), Chroma Blue Difference (Cb), and Chroma Red Difference (Cr) (YCbCr) color space to generate a low-resolution (LR) residual image. A plurality of convolutional layers of a neural network model is applied to the LR residual image to convert it to a plurality of HR residual sub-images corresponding to the digital image. An HR image corresponding to the digital image is generated using the base HR image and the plurality of HR residual sub-images.