公开(公告)号：US20170278220A1

公开(公告)日：2017-09-28

申请号：US15126271

申请日：2016-03-17

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Lerenhan LI ,  Nong SANG ,  Changxin GAO ,  Luxin YAN ,  Jin WANG ,  Shiwei ZHANG ,  Yuanjie SHAO ,  Juncai PENG

IPC: G06T5/00 ,  G06T7/00 ,  G06T3/40

CPC classification number: G06T5/002 ,  G06T3/4007 ,  G06T3/4076 ,  G06T7/0002 ,  G06T2207/10048 ,  G06T2207/20076

Abstract: The invention discloses a method for correcting aero-optical thermal radiation noise, comprising steps of: pretreating a degraded image to obtain a multi-scale degraded image group, conducting iteration process of obtaining an optimal solution by using a last scale estimation result as an original value of next scale estimation according to the multi-scale degraded image group, thereby facilitating original-scale bias field estimation, and restoring the degraded image according to the original-scale bias field estimated value thereby obtaining an image after aero-optical thermal radiation noise correction. The invention also discloses a system for correcting aero-optical thermal radiation noise. The invention is capable of solving problems with conventional methods, comprising poor correction effect, high complexity, and incapability in correcting the thermal radiation noise at an image level, and applicable to restoration of an image with aero-optical thermal radiation noise.

公开(公告)号：US20180068430A1

公开(公告)日：2018-03-08

申请号：US15709454

申请日：2017-09-19

Applicant: HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY

Inventor： Nong SANG ,  Lerenhan LI ,  Luxin YAN ,  Changxin GAO ,  Yuanjie SHAO ,  Juncai PENG ,  Shiwei ZHANG ,  Jin WANG

IPC: G06T7/00 ,  G06N3/02 ,  G06F17/11 ,  G06F17/18

CPC classification number: G06T7/0002 ,  G06F17/11 ,  G06F17/18 ,  G06K9/40 ,  G06K9/4628 ,  G06N3/02 ,  G06T5/003 ,  G06T2207/20076 ,  G06T2207/20081 ,  G06T2207/20084

Abstract: A method for estimating a blur kernel size, the method including: (1) pre-processing a blurred image, to obtain an image, so that a size of the image satisfies an image input size of a multi-class convolutional neural network (CNN); (2) inputting the image into a multi-class CNN with completed training, to obtain a blur-kernel-size probability distribution vector; and (3) comparing each element in the blur-kernel-size probability distribution vector, so that an estimated blur kernel size of the blurred image is the blur kernel size corresponding to a largest element. The invention also provides a system for estimating a blur kernel size. The system includes an image pre-processing module, a training-set synthesizing module, a multi-class CNN module, and a blur kernel size estimation module.