| Paper: | TP-P5.7 |
| Session: | Image and Video Artifact Removal and Denoising |
| Time: | Tuesday, September 18, 14:30 - 17:10 |
| Presentation: |
Poster
|
| Title: |
ITERATIVE BLIND IMAGE MOTION DEBLURRING VIA LEARNING A NO-REFERENCE IMAGE QUALITY MEASURE |
| Authors: |
Wen-Hao Lee; National Tsing Hua University | | |
| | Shang-Hong Lai; National Tsing Hua University | | |
| | Chia-Lun Chen; Industrial Technology Research Institute | | |
| Abstract: |
In this paper, we propose a learning-based image restoration algorithm for restoring images degraded by uniform motion blurs. The motion blur parameters are first approximately estimated from the robust global motion estimation result. Then, we present a novel framework to refine the image restoration iteratively based on recursively adjusting the motion blur parameters for image restoration to achieve the best image quality measure. Note that a no-reference image quality assessment model is learned by training a RBF neural network from a collection of representative training images simulated with different motion blurs. Experimental results blured on real videos are given to demonstrate the performance of the proposed blind motion deblurring algorithm. |
