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Removing Camera Shake from a Single Photograph

Removing Camera Shake from a Single Photograph. 报告人 ：牟加俊 日期： 2013-12-13. In ACM SIGGRAPH, 2006. Content. I ntroduction Image Restoration (2) I ntroduction the method in this paper (3) E xperiments. Image Restoration. Restoration. WHAT?. 客观过程 (an objective process).

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Removing Camera Shake from a Single Photograph

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

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

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

  5. Image blurs and PSF THEN？ 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 SO Spatial convolution form Frequency convolution form Matrix-vector form

  7. Image restoration = Image deblurring = Image deconvolution Image Restoration HOW? 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。

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

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

  10. Example Wiener filtering Inverse filtering with cut-off frequency 70 Inverse 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 One contribution!! 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 distributionsrespectively

  14. estimate blur kernel maximum a-posteriori (MAP) solution:finds the kernel and latent image gradients that maximizes THE SECOND CONTRIBUTION!! Using Miskin andMacKay's algorithm : Minimizesthe distance between the approximating distribution and the trueposterior.

  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) Makeuse of more advanced natural image statistics 2) applying modern statistical methods to the non-blind deconvolution problem. 3) employing more exhaustivesearch procedures, or heuristics to guess the relevant parameters

  20. Thank you

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