IP Library › Granted Patent US 12,608,880
Granted Patent B2
US 12,608,880 · App. 18/161,780 · Granted Apr 21, 2026

View extrapolation method using epipolar plane image

Inventors: Hae-Gon Jeon (Gwangju, KR); Hyunjun Jung (Gwangju, KR)
Assignee: GIST (Gwangju Institute of Science and Technology)
G06T15/205G06N3/0475G06T3/18G06T3/4046G06T3/4053G06T5/77G06T7/557G06T2207/10052G06T2207/20084G06T2207/20228
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Quick Facts
Patent No.
US 12,608,880
App. No.
18/161,780
Granted
Apr 21, 2026
Kind
B2
Abstract

The present disclosure relates to a method of inferring an epipolar plane image and extrapolating a view of a target image using the epipolar plane image. A view extrapolation method using an epipolar plane image according to an embodiment of the present disclosure includes: creating an Epipolar Plane Image (EPI) using a target image and a depth map corresponding to the target image; creating a super-resolution EPI and a disparity of the EPI on the basis of the EPI; creating an EPI mask by warping the super-resolution EPI in accordance with the disparity; and creating a restored EPI by applying the EPI mask to the warped super-resolution EPI, and creating an extrapolated image for the target image using the restored EPI.

Claims (27)

1 . A view extrapolation method using an epipolar plane image, the view extrapolation method comprising:

creating an Epipolar Plane Image (EPI) using a target image and a depth map corresponding to the target image;

creating a super-resolution EPI and a disparity of the EPI on the basis of the EPI;

creating an EPI mask by warping the super-resolution EPI in accordance with the disparity; and

creating a restored EPI by applying the EPI mask to the warped super-resolution EPI, and creating an extrapolated image for the target image using the restored EPI,

wherein the creating of an EPI mask includes creating an EPI mask of which a value is determined by a coordinate of a pixel that is propagated in accordance with the disparity when the super-resolution EPI is warped,

wherein the creating of a restored EPI includes:

creating a masked EPI by performing element-wise multiplication on the EPI mask and the warped super-resolution EPI; and

creating the restored EPI by restoring the masked EPI, and

wherein the creating of a restored EPI includes creating the restored EPI by inputting the masked EPI to a Generative Adversarial Network (GAN).

2 . The view extrapolation method of claim 1 ,

wherein the creating of an EPI comprises:

creating a plurality of stacked sub-aperture images through warping according to the depth map; and

creating the EPI from the plurality of stacked sub-aperture images by axially cutting the plurality of stacked sub-aperture images.

3 . The view extrapolation method of claim 1 , wherein the creating of an EPI includes:

receiving the target image taken at a first view and a reference image taken at a second view; and

creating the depth map corresponding to the target image on the basis of the target image and the reference image.

4 . The view extrapolation method of claim 1 , wherein the creating of an EPI includes creating the EPI by applying Gaussian blur to the depth map.

5 . The view extrapolation method of claim 1 , wherein the creating of a disparity includes creating the disparity on the basis of an inclination of the EPI.

6 . The view extrapolation method of claim 1 , wherein the warping of the super-resolution EPI includes warping pixels of each of lines constituting the super-resolution EPI in accordance with the disparity of each of the lines.

7 . The view extrapolation method of claim 6 , wherein the warping of the super-resolution EPI includes:

determining a pixel value of a first line of the super-resolution EPI as a pixel value of a first line of the warped super-resolution EPI; and

determining the warped super-resolution EPI by propagating pixel values of each of lines of the warped super-resolution EPI to next lines in accordance with the disparity of each of the lines.

8 . The view extrapolation method of claim 1 , wherein the creating of an EPI mask includes creating a binary EPI mask in which the value of a coordinate of a pixel that is propagated in accordance with the disparity is 1 and values of coordinates of the other pixels are 0.

9 . The view extrapolation method of claim 1 , wherein the creating of an extrapolated image includes:

creating a plurality of sub-aperture images using the restored EPI; and

determining any one of the plurality of sub-aperture images as the extrapolated image.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 30, 2023
From: JEON, HAE-GON; JUNG, HYUNJUN
To: GIST (GWANGJU INSTITUTE OF SCIENCE AND TECHNOLOGY)
Reel/Frame 062537/0331 →
Priority Claims (1)
KR 10-2022-0021512 · Feb 18, 2022 · national
Continuity (1)
Related Publication 20230267572A1 · Aug 24, 2023
References Cited (14)
US 20180342075A1 · Wang · 2018 [cited by examiner]
US 20230106939A1 · Yuan · 2023 [cited by examiner]
Wu, Gaochang, et al. “Light field reconstruction using convolutional network on EPI and extended applications.” IEEE transactions on pattern analysis and machine intelligence 41.7 (2018): 1681-1694. (Year: 2018). [cited by examiner]
Wu, Gaochang, et al. “Light field image processing: An overview.” IEEE Journal of Selected Topics in Signal Processing 11.7 (2017): 926-954. (Year: 2017). [cited by examiner]
Tam, Wa James, et al. “Smoothing depth maps for improved steroscopic image quality.” Three-Dimensional TV, Video, and Display III. vol. 5599. SPIE, 2004. (Year: 2004). [cited by examiner]
Lv, Huijin, et al. “Light field depth estimation exploiting linear structure in EPI.” 2015 IEEE International Conference on Multimedia & Expo Workshops (ICMEW). IEEE, 2015. (Year: 2015). [cited by examiner]
Ding, Yuyang, et al. “Rain streak removal from light field images.” IEEE Transactions on Circuits and Systems for Video Technology 32.2 (2021): 467-482. (Year: 2021). [cited by examiner]
Elharrouss, Omar, et al. “Image inpainting: A review.” Neural Processing Letters 51 (2020): 2007-2028. (Year: 2019). [cited by examiner]
Tran, Trung-Hieu, Jan Berberich, and Sven Simon. “3DVSR: 3D EPI vol. based Approach for Angular and Spatial Light field Image Super-resolution.” arXiv preprint arXiv:2201.01294v1 (2022). (Year: 2022). [cited by examiner]
Wanner, Sven, and Bastian Goldluecke. “Variational light field analysis for disparity estimation and super-resolution.” IEEE transactions on pattern analysis and machine intelligence 36.3 (2013): 606-619. (Year: 2013). [cited by examiner]
Changha Shin et al., “EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images”, Computer Vision and Pattern Recognition, Apr. 6, 2018, total 10 pages, doi: arxiv.org/abs/18… [cited by applicant]
Jinglei Shi et al., “Learning Fused Pixel and Feature-based View Reconstructions for Light Fields”, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), (2020), date of conference: Ju… [cited by applicant]
An Office Action mailed by the Korean Intellectual Property Office on Jun. 20, 2023, which corresponds to Korean Patent Application No. 10-2022-0021512 and is related to U.S. Appl. No. 18/161,780. [cited by applicant]
A Notice of Allowance mailed by the Korean Intellectual Property Office on Jul. 19, 2023, which corresponds to Korean Patent Application No. 10-2022-0021512 and is related to U.S. Appl. No. 18/161,780. [cited by applicant]