A system can receive downhole acquisition data relating to a wellbore. The system can pre-process the downhole acquisition data. The system can generate an incomplete borehole image using the downhole acquisition data. The system can determine a sparse representation based on the incomplete borehole image by performing an optimization with respect to the incomplete borehole image. The system can generate a complete borehole image based on an inverse of the sparse representation.

An image processing apparatus includes an input unit that inputs an image, and a processor configured to read out a program stored in a memory, and executes the program. The processor is configured to detect an intended subject from the input image by a first detection method, set an intended subject region for the detected intended subject, detect the intended subject from the input image by a second detection method different from the first detection method, and update the set intended subject region by using a detection result of the second detection method.

Disclosed are systems and associated methods for generating a regularized image from non-uniformly distributed image data based on a curve derived from the image data. The curve is used to reduce the non-uniformity in the image data without losing detail or changing the overall image. Regularizing the image includes obtaining a tree-based representation of the non-uniformly distributed image data, generating a regularization curve that models a particular distribution of the image data, and applying the regularization curve to the tree-based representation in order to select the nodes of the tree-based representation for the decimated image data to render in place of the original image data and the original image data to preserve and render as part of the regularized image. Specifically, the system render the original image data associated with leaf nodes and the decimated image data associated with parent nodes that intersect or are within the regularization curve.

A computer-implemented method for sampling and analyzing data from at least one image frame from at least one series of image frames captured by at least one sensor, comprises: defining at least one sampling model, wherein the sampling model is defined in a virtual 3D-vector space and is based on one or more predetermined shapes in the virtual 3D-vector space, applying the at least one sampling model to at least one part of the at least one image frame of the at least one series of image frames, wherein applying of the at least one sampling model defines at least one area of the at least one image frame from which data is to be extracted, extracting data from the at least one area of the at least one image frame defined by the sampling model, and analyzing the extracted data.

The technology employs a patch-based multi-scale Transformer (300) that is usable with various imaging applications. This avoids constraints on image fixed input size and predicts the quality effectively on a native resolution image. A native resolution image (304) is transformed into a multi-scale representation (302), enabling the Transformer's self-attention mechanism to capture information on both fine-grained detailed patches and coarse-grained global patches. Spatial embedding (316) is employed to map patch positions to a fixed grid, in which patch locations at each scale are hashed to the same grid. A separate scale embedding (318) is employed to distinguish patches coming from different scales in the multiscale representation. Self-attention (508) is performed to create a final image representation. In some instances, prior to performing self-attention, the system may prepend a learnable classification token (322) to the set of input tokens.

According to an embodiment of the disclosure, an electronic device may include: a display, a memory, and a processor operatively connected to the display and the memory. According to an embodiment, the memory may store instructions that, when executed, cause the processor to: obtain a first image of a first shape, obtain linear information indicating a morphological characteristic of an object in the first image of the first shape, determine a conversion method for converting the first image of the first shape into an image of a second shape based on the obtained linear information, convert the first image of the first shape into a second image of the second shape based on the determined conversion method, and control the display to display the converted second image of the second shape on the display.

An unsupervised real to virtual domain unification model for highway driving, or DU-drive, employs a conditional generative adversarial network to transform driving images in a real domain to their canonical representations in the virtual domain, from which vehicle control commands are predicted. In the case where there are multiple real datasets, a real-to-virtual generator may be independently trained for each real domain and a global predictor could be trained with data from multiple real domains. Qualitative experiment results show this model can effectively transform real images to the virtual domain while only keeping the minimal sufficient information, and quantitative results verify that such canonical representation can eliminate domain shift and boost the performance of control command prediction task.

An electronic device may include an electronic display to display an image based on processed image data. The electronic device may also include image processing circuitry to determine a hierarchical grid having multiple grid points divided into grid partitions. A first set of grid points associated with a first set of grid partitions may include a first set of mappings to corresponding coordinates of input image data in a source frame. The image processing circuitry may also interpolate between the first set of grid points to determine a second set of grid points of having a second set of mappings to corresponding coordinates of the input image data based on the first set of mappings. The image processing circuitry may also generate the processed image data by applying the first set of mappings and the second set of mappings to the input image data.

Information about a drug-containing vessel is determined by capturing image data of the curved surface of a cylindrical portion of a drug-containing vessel. The image data is unfurled from around the curved surface, binarised, and a template matching algorithm employed to determine that the label information comprises candidate information about the vessel and/or the drug.

In accordance with some embodiments of the disclosed subject matter, methods, systems, and media for generating images of multiple sides of an object are provided. In some embodiments, a method comprises receiving information indicative of a 3D pose of a first object in a first coordinate space at a first time; receiving a group of images captured using at least one image sensor, each image associated with a field of view within the first coordinate space; mapping at least a portion of a surface of the first object to a 2D area with respect to the image based on the 3D pose of the first object; associating, for images including the surface, a portion of that image with the surface of the first object based on the 2D area; and generating a composite image of the surface using images associated with the surface.