公开(公告)号：US20230196721A1

公开(公告)日：2023-06-22

申请号：US17999467

申请日：2021-06-09

Applicant: SANECHIPS TECHNOLOGY CO., LTD.

Inventor： Cong REN ,  Ke XU ,  Dehui KONG ,  Jing YOU ,  Xin LIU ,  Fang ZHU

IPC: G06V10/60 ,  G06T3/00 ,  G06T3/40 ,  G06V10/24 ,  H04N19/513

CPC classification number: G06V10/60 ,  G06T3/40 ,  G06T3/0093 ,  G06V10/24 ,  H04N19/513

Abstract: A low-illuminance video processing method, a low-illuminance video processing device and a storage medium are disclosed. The method includes: acquiring a same number of preceding frame images and subsequent frame images corresponding to a current video frame of a low-illuminance video to obtain a frame image set corresponding to the current video frame, and performing traversal on the low-illuminance video to obtain frame image sets corresponding to all video frames; after performing image alignment on all frame images in the frame image sets corresponding to all video frames, inputting the frame image sets into a pre-trained low-illuminance image enhancement model to obtain enhanced frame images; and generating an enhanced video based on the enhanced frame images.

公开(公告)号：US20240394836A1

公开(公告)日：2024-11-28

申请号：US18694974

申请日：2022-03-16

Applicant: SANECHIPS TECHNOLOGY CO., LTD.

Inventor： Cong REN ,  Hengqi LIU ,  Ke XU ,  Dehui KONG ,  Jisong AI ,  Xin LIU ,  Jing YOU

IPC: G06T5/20

Abstract: The present disclosure provides a method for training an image enhancement model, the image enhancement model includes an enhancement module including convolution branches corresponding to brightness intervals; and the method includes: inputting a sample image to the image enhancement model, and acquire a result image output by the image enhancement model; calculating losses including an image loss of the result image relative to a Ground Truth image, and a first constraint loss of brightness histogram constraint of each of the convolution branches of an image output from each of the convolution branches relative to the Ground Truth image; adjusting the enhancement module according to the losses; and in a case where a training end condition is not met, returning to the operation of inputting the sample image to the image enhancement model. The present disclosure further provides an image enhancement method and a computer-readable medium.

公开(公告)号：US20230177650A1

公开(公告)日：2023-06-08

申请号：US17924158

申请日：2021-05-06

Applicant: SANECHIPS TECHNOLOGY CO., LTD.

Inventor： Jing YOU ,  Hengqi LIU ,  Ke XU ,  Dehui KONG ,  Jisong AI ,  Xin LIU ,  Cong REN

IPC: G06T5/00 ,  G06T3/40

CPC classification number: G06T5/001 ,  G06T3/40 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: Disclosed are a method and apparatus for training an image restoration model, an electronic device, and a computer-readable storage medium. The method for training an image restoration model includes: pre-processing training images to obtain a low-illumination image sample set (110); determining, based on low-illumination image samples in the low-illumination image sample set and the image restoration model, a weight coefficient of the image restoration model (120), wherein the image restoration model is a neural network model determined on a U-Net network and a deep residual network; and adjusting the image restoration model according to the weight coefficient, and further training the adjusted image restoration model using the low-illumination image samples until the image restoration model restores parameters of all the low-illumination image samples in the low-illumination image sample set into a preset range (130).