1. Removing Camera Shake from a Single Photograph 报告人：牟加俊 日期： 2013-12-13 In ACM SIGGRAPH, 2006.

2. Content (1)Introduction Image Restoration (2) Introduction the method in this paper (3) Experiments

3. Image Restoration Restoration 客观过程 (an objective process) “ 图像恢复 ” 是根据某最优准则，使 得恢复后的图像是对理想图像的最 佳逼近。

4. Image blurs and PSF Global blurs: camera shake Local blurs: object moving What ‘s motion blur? Motion blur results from relatively large motion between the camera and the object. 相对运动

5. Image blurs and PSF Total exposure=  (instantaneous exposure) 模糊图像＝理想的局部积分 Point Spread Function:If the ideal image would consist of a single intensity point or point source  (x,y)=1, this point would be recorded as a spread-out intensity pattern 。

6. Model of the Image Degradation

7. Image Restoration Blind image deconvolution （ BID 盲去卷积）：在 模糊核未知的条件下恢复出清晰的图像。 Non-blind image deconvolution （ NBID 非盲去卷 积）： inverse filtering ( 逆滤波 ) 、 Wiener filtering ( 维纳滤波 ) 、 Richardson-Lucy 方法等。 Image restoration Image deblurring Image deconvolution = =

8. Inverse Filtering Ignore noise N(u,v) Drawback ：

9. Wiener filtering 求 Wopt(u,v) 使得均方差 =min Wiener filtering ( 维纳滤波 )= 最小均方差滤波 已知 Wiener 给出的解是 : 退化函数 噪声功率谱 理想图像功率谱

10. Example Inverse filtering Inverse filtering with cut-off frequency 70 Wiener filtering

11. Image model B ： blurred input image K ： blur kernel L ： latent image N ： sensor noise Two main steps: 1: estimate blur kernel; 2:deblur.

12. estimate blur kernel The distribution over gradient magnitudes obey heavy-tailed distributions; The distribution can be represented with a zero mean mixture-of-Gaussians model

13. estimate blur kernel Given the grayscale blurred patch, estimate K and the latent patch image N and E denote Gaussian and Exponential distributions respectively

14. estimate blur kernel maximum a-posteriori (MAP) solution:finds the kernel and latent image gradients that maximizes Using Miskin and MacKay's algorithm : Minimizes the distance between the approximating distribution and the true posterior.

15. Multi-scale approach perform estimation by varying image resolution in a coarse-to-fine manner

16. Multi-scale approach At last, reconstruct the latent color image L with the Richardson-Lucy (RL) algorithm

17. Experiments

18. Conclusion There are many improvements spaces: 1) ringing artifacts occur near saturated regions and regions of significant object motion. 2) There are a number of common photographic effects that we do not explicitly model, including saturation, object motion, and compression artifacts. 3) this method requires some manual intervention.

19. Conclusion Solution: 1) Make use of more advanced natural image statistics 2) applying modern statistical methods to the non-blind deconvolution problem. 3) employing more exhaustive search procedures, or heuristics to guess the relevant parameters

20. Thank you