公开(公告)号：US11611697B2

公开(公告)日：2023-03-21

申请号：US17263743

申请日：2019-08-06

Applicant: Google LLC

Inventor： Yi Hung Chen ,  Chia-Kai Liang ,  Bartlomiej Maciej Wronski ,  Peyman Milanfar ,  Ignacio Garcia Dorado

IPC: H04N5/232

Abstract: The present disclosure describes systems and techniques directed to optical image stabilization movement to create a super-resolution image of a scene. The systems and techniques include a user device (102) introducing (502), through an optical image stabilization system (114), movement to one or more components of a camera system (112) of the user device (102). The user device (102) then captures (504) respective and multiple frames (306) of an image of a scene, where the respective and multiple frames (306) of the image of the scene have respective, sub-pixel offsets of the image of the scene across the multiple frames (306) as a result of the introduced movement to the one or more components of the camera system (112). The user device (102) performs (506), based on the respective, sub-pixel offsets of the image of the scene across the respective, multiple frames (306), super-resolution computations and creates (508) the super-resolution image of the scene based on the super-resolution computations.

公开(公告)号：US20200186836A1

公开(公告)日：2020-06-11

申请号：US16210900

申请日：2018-12-05

Applicant: Google LLC

Inventor： Peyman Milanfar ,  Feng Yang ,  Sungjoon Choi

IPC: H04N19/625 ,  H04N19/124 ,  H04N19/184

Abstract: Methods are provided for sharpening or otherwise modifying compressed images without decompressing and re-encoding the images. An overall image quality is determined based on the source of the compressed image, the quantization table of the compressed image, or some other factor(s), and a set of scaling factors corresponding to the image quality is selected. The selected scaling factors are then applied to corresponding quantization factors of the image's quantization table or other parameters of the compressed image that describe the image contents of the compressed image. The scaling factors of a given set of scaling factors can be determined by a machine learning process that involves training the scaling factors based on training images determined by decompressing and then sharpening or otherwise modifying a source set of compressed images. These methods can provide improvements with respect to encoded image size and computational cost of the image modification method.