Single-Image Refocusing and Defocusing

1. Single-Image Refocusing and Defocusing Wei Zhang, Nember, IEEE, and Wai-Kuen Cham, Senior Member, IEEE

2. Outline • Introdution • Background And Problem Fomulation • Edge-Based Focus-Map Estimation • Image Refocusing By Blind Deconvolution • Experiment And Discussions • Conclusion

3. Outline • Introdution • Background And Problem Fomulation • Edge-Based Focus-Map Estimation • Image Refocusing By Blind Deconvolution • Experiment And Discussions • Conclusion

4. Introduction • One is image refocusing, which is to recover the sharpness of the blurry defocused objects in an input image and to generate a virtual all-focused image. • The other is defocusing, which is to blur an image and to create defocus effects. • The major novelty of this paper is introducing a tractable SBD framework, which can apply separately these two constraints to ensure image sharpness and to suppress ring artifacts.

5. Introduction • (a) Input narrow-aperture image focusing on the foreground object. • (b) Synthesized image with shallower DOF. • (c) Synthesized all-focused image. • (d) Synthesized image focusing on the background. • (e) Detected focus mask (white: defocused regions, black: focused regions, gray: focus boundaries). • (f) Close-up comparison. Left: removing the lens blur using the lens deblurringin smart sharpen of Photoshop. Right: Our refocused result.

6. Outline • Introdution • Background And Problem Fomulation • Edge-Based Focus-Map Estimation • Image Refocusing By Blind Deconvolution • Experiment And Discussions • Conclusion

7. Background And Problem Formulation • Image Model • Such blurring process is often modeled as the convolution of the focused image F with PSF. • I denotes the defocused image and n is the noise term

8. Background And Problem Formulation • Due to the diffraction and aberration of the camera lens, the PSFis approximated normally by a 2-D Gaussian filter, given by • The spread parameter , which is related to the distance of the object to the focal plane, determines the blurriness of the captured image.

9. Background And Problem Formulation • Edge Modeling • A step edge x0at can be represented by e(x;b,c,x0) = cU(x - x0) + b, where U(‧) is the unit step function. b denotes the edge basis. c represents the edge contrast.

10. Background And Problem Formulation • The typical edge s(x;b,c,w,x0) can be regarded as a smoothed step edge e(x;b,c,x0), which is obtained by convolving with the 1-D Gaussian filter and therefore is as follows • Parameters

11. Outline • Introdution • Background And Problem Fomulation • Edge-Based Focus-Map Estimation • Image Refocusing By Blind Deconvolution • Experiment And Discussions • Conclusion

12. Edge-Based Focus-Map Estimation • Based on the above edge model, a method is proposed in this section to estimate automatically the focus map for an image containing a mixture of focused and defocused objects. • Our proposed method is also based on this assumption and thus shares the common limitation that it cannot estimate an accurate focus map for natural blurry objects such as clouds and shadows. • By contrast, our proposed method is simpler and has lower computational complexity since all edge parameters are derived in closed form.

13. Edge-Based Focus-Map Estimation

14. Outline • Introdution • Background And Problem Fomulation • Edge-Based Focus-Map Estimation • Image Refocusing By Blind Deconvolution • Experiment And Discussions • Conclusion

15. Image Refocus By Blind Deconvolutiob • 1)It is utilized for PSF estimation. • 2)An edge sharpness prior is developed to constrain the PSF not to blur the edges and enforce the refocusing image to agree with the precalculated sharpened image in the vicinity of edges. • 3)The proposed SBD method will be presented by assuming that the PSF is spatially invariant for the sake of simplicity.

16. Image Refocus By Blind Deconvolutiob • Let be F the refocused image of the blurry image I . F Is expected to satisfy two conditions: (1)The edges should become sharpened in F (2) The locally smooth regions in I should remain almost unchanged in F .

17. Outline • Introdution • Background And Problem Fomulation • Edge-Based Focus-Map Estimation • Image Refocusing By Blind Deconvolution • Experiment And Discussions • Conclusion

18. Experiments And Discussions • (a) Estimated focus map. (b) Blurriness of the pixels at the dashed line of (a). (c) Segmentation result based on (a).

19. Experiments And Discussions

20. Experiments And Discussions

21. Outline • Introdution • Background And Problem Fomulation • Edge-Based Focus-Map Estimation • Image Refocusing By Blind Deconvolution • Experiment And Discussions • Conclusion

22. Conclusion • The proposed SBD is free of user initialization and has low computational complexity. • A wide variety of images has been tested to validate the proposed algorithm. • In the future, we intend to use optimized GPU programming to accelerate further the speed of the algorithm. • Finally, we would like to extend the basic idea of this paper to solve other low-level vision problems such as spatially variant deblurring.

23. Thank you for your listening