Removing Partial Blur in a Single Image

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

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

3. Introduction • Two key issues • Partial blur estimation • Partial deblurring

4. 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

5. 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

6. 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

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

8. 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

9. Experiments

12. 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