Iterative Blind Image Motion Deblurring via Learning a No-Reference Image Quality Measure

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.

• Type: Conference
• Year: 2007
• Where: ICIP
• Authors: Wen-Hao Lee, Shang-Hong Lai, Chia-Lun Chen
• Keywords: ICIP 2007, Image Processing, Image Restoration Algorithm, Motion Blur Parameters, Motion Deblurring Algorithm, Uniform Motion Blurs