Removing Partial Blur in a Single Image Shengyang Dai and Ying Wu EECS Department, Northwestern University, Evanston, IL 60208, USA 2009CVPR

Outline Introduction Generation model of partial blur ◦ The two-layer model for a clear image ◦ Motion blur ◦ Out-of-focus blur ◦ Unified formulation of partial blurs Image recovery from partial degradation ◦ The objective function ◦ Initialization ◦ Recovering (F, B, α) Experiments Conclusion

Introduction Two key issues ◦ Partial blur estimation ◦ Partial deblurring

Generation model of partial blur(1/3) The two-layer model for a clear image ◦ I = F α + B(1 − α) Degraded image is the average over time F α : clear foreground component B(1 − α) : clear background component α : clear soft occlusion mask,α(x) ∈ [0, 1] for each pixel x

Generation model of partial blur(2/3) Motion blur ◦ Case 1:  foreground object is moving  static background, we have = 0, q = δ. ◦ Case 2:  background is moving  static foreground, we have = 0, p = δ. Out-of-focus blur ◦ Case 1:  background layer is in focus  foreground layer is out- of-focus ◦ Case 2:  foreground layer is in focus  background layer is out- of-focus

Generation model of partial blur(3/3) Unified formulation of partial blurs Either the foreground or background layer is not degraded ◦ p or q is the δ function

Image recovery from partial degradation(1/2) The objective function

Image recovery from partial degradation(1/2) Initialization ◦ extract the degraded occlusion mask by using a matting technique ◦ the degradation kernels p and q are estimated by analyzing both and ◦ iterate between F, B and α to obtain the final recovery

Experiments

Conclusion Removing partial blur from a single image input A two-layer image model ◦ foreground and background layers Enables high quality recovery and synthesis for real images