APPARATUS AND METHOD FOR GENERATING TOMOGRAPHY IMAGE
    4.
    发明申请
    APPARATUS AND METHOD FOR GENERATING TOMOGRAPHY IMAGE 有权
    用于产生TOMOGRAPHY图像的装置和方法

    公开(公告)号:US20150131104A1

    公开(公告)日:2015-05-14

    申请号:US14469692

    申请日:2014-08-27

    IPC分类号: G01B9/02

    摘要: Provided are a tomography image generating method and an apparatus for generating a tomography image. The method of generating a tomography image includes, in response to a depth scan operation performed on an object, generating a candidate tomography image by using an interference signal acquired by the performed depth scan operation, determining a pixel pattern by using the generated candidate tomography image; and when the depth scan operation performed on the object is completed, generating a final tomography image of the object by using a finally determined pixel pattern. The generating the candidate tomography image and the determining are parallel processed by at least one processor during the depth scan operation being repeatedly performed.

    摘要翻译: 提供了一种断层摄影图像生成方法和用于产生断层摄影图像的装置。 生成断层摄影图像的方法包括响应于对物体进行的深度扫描操作,通过使用由所执行的深度扫描操作获取的干涉信号来生成候选断层摄影图像,通过使用所生成的候选层析成像图像来确定像素图案 ; 并且当对物体进行深度扫描操作完成时,通过使用最终确定的像素图案来生成对象的最终断层图像。 在重复执行深度扫描操作期间,生成候选断层摄影图像和确定由至少一个处理器并行处理。

    DEVICE AND METHOD WITH INCREASING RESOLUTION OF FRAME IN G-BUFFER DOMAIN

    公开(公告)号:US20240249383A1

    公开(公告)日:2024-07-25

    申请号:US18351963

    申请日:2023-07-13

    IPC分类号: G06T3/40 G06T3/00

    摘要: A method includes: inserting new pixels between original pixels for each of maps included in a first geometry buffer (or G-buffer) generated from a frame, wherein the maps represent geometric information of a three-dimensional (3D) model of an object included in the frame; generating a second G-buffer by setting values of the new pixels using a motion vector map that may be one of the maps; generating a third G-buffer by combining, with the second G-buffer, a result of updating only values of pixels masked based on an output of a pixel masking neural network to which the second G-buffer may be input; generating a fourth G-buffer by updating values of pixels by inputting the third G-buffer to a G-buffer reconstruction neural network; and update, based on the fourth G-buffer, the resolution of a subsequent frame that follows the frame.

    METHOD AND APPARATUS WITH IMAGE PROCESSING
    8.
    发明公开

    公开(公告)号:US20240212089A1

    公开(公告)日:2024-06-27

    申请号:US18528979

    申请日:2023-12-05

    摘要: A method and apparatus with image processing is provided. The processor-implemented method includes generating a warped image frame by warping a first reconstructed image frame of a first time point based on first change data corresponding to a change between first rendered image frame of the first time point and second rendered image frame of a second time point that is different from the first time point; generating, using a neural reconstruction model based on the second rendered image frame and the warped image frame, a confidence map representing a second reconstructed image frame of the second time point and confidence scores of pixels of the second reconstructed image frame; and generating a third rendered image frame of a third time point, different from the first and second time points, by ray tracing for each of plural pixels of the third rendered image frame based on the confidence map.

    METHOD AND APPARATUS WITH ADAPTIVE SUPER SAMPLING

    公开(公告)号:US20240161236A1

    公开(公告)日:2024-05-16

    申请号:US18316120

    申请日:2023-05-11

    IPC分类号: G06T3/40 G06T7/246 G06V10/75

    摘要: An adaptive super sampling method includes: rendering frames of a three-dimensional (3D) model, the frames including a current frame and a previous frame preceding the current frame; determining motion vectors indicating a correspondence relationship between pixels in the current frame and pixels in the previous frame; generating a geometric identifier maps (G-ID maps) respectively corresponding to the current frame and the previous frame based on 3D geometrical properties associated with the pixels in the current frame and the previous frame; based on the motion vectors and the G-ID maps, generating an artifact map predicting where artifacts will occur from inter-frame super sampling of the current frame; and performing adaptive super sampling on the current frame based on the artifact map.