TIIS (한국인터넷정보학회)
Current Result Document : 6 / 6
| 한글제목(Korean Title) |
A depth-based Multi-view Super-Resolution Method Using Image Fusion and Blind Deblurring |
| 영문제목(English Title) |
A depth-based Multi-view Super-Resolution Method Using Image Fusion and Blind Deblurring |
| 저자(Author) |
Jun Fan
Xiangrong Zeng
Qizi Huangpeng
Yan Liu
Xin Long
Jing Feng
Jinglun Zhou
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| 원문수록처(Citation) |
VOL 10 NO. 10 PP. 5129 ~ 5152 (2016. 10) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Multi-view super-resolution (MVSR) aims to estimate a high-resolution (HR) image from a set of low-resolution (LR) images that are captured from different viewpoints (typically by different cameras). MVSR is usually applied in camera array imaging. Given that MVSR is an ill-posed problem and is typically computationally costly, we super-resolve multi-view LR images of the original scene via image fusion (IF) and blind deblurring (BD). First, we reformulate the MVSR problem into two easier problems: an IF problem and a BD problem. We further solve the IF problem on the premise of calculating the depth map of the desired image ahead, and then solve the BD problem, in which the optimization problems with respect to the desired image and with respect to the unknown blur are efficiently addressed by the alternating direction method of multipliers (ADMM). Our approach bridges the gap between MVSR and BD, taking advantages of existing BD methods to address MVSR. Thus, this approach is appropriate for camera array imaging because the blur kernel is typically unknown in practice. Corresponding experimental results using real and synthetic images demonstrate the effectiveness of the proposed method. |
| 키워드(Keyword) |
Multi-view super-resolution
depth estimation
graph cuts
blind deblurring
ADMM
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| 파일첨부 |
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