公开(公告)号：US11055819B1

公开(公告)日：2021-07-06

申请号：US16143925

申请日：2018-09-27

Applicant: Amazon Technologies, Inc.

Inventor： Kunwar Yashraj Singh ,  Paulo Mendonca

IPC: G06T3/40 ,  G06T5/00

Abstract: Techniques for machine learning-based image super-resolution are described. A Dual Path Deep Back Projection Network can be used to enhance an input image. For example, the model may be trained to perform image super-resolution, remove artifacts, provide filtering or low light enhancement, etc. Classification may be performed on the resulting enhanced images to identify objects represented in the images. The model may be trained using a dataset that includes groups of images: an original image and an enhanced image. The model may use both residual and dense connectivity patterns between each successive back projection blocks to improve construction of a high-resolution output image from a low resolution input image. The enhanced images increase classification accuracy for input images having low image resolution.

公开(公告)号：US10796476B1

公开(公告)日：2020-10-06

申请号：US16119514

申请日：2018-08-31

Applicant: Amazon Technologies, Inc.

Inventor： Yifan Xing ,  Paulo Mendonca ,  Rahul Tewari

IPC: G06T15/08 ,  G06T15/04 ,  G06T17/20

Abstract: Techniques for improving a 2D to 3D image reconstruction network machine learning model are described. In some instances, this includes performing at least two transformations of a 3D model to generate at least two rotated 3D models, the at least two transformations to rotate the 3D model about an axis away from a viewing direction of the single 2D image; rendering the at least two rotated 3D models as rendered 2D images; and retraining a 2D to 3D image reconstruction network machine learning model using corresponding pairs of rotated 3D models and rendered 2D images.