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Image Enhancement

Image Enhancement. Author: Ying Qu, Michael Vaughan Adviser: Professor Mongi A. Abidi IRIS Lab June 8 , 2015. Process Pipeline. Image Super Resolution. Image Blind Deconvolution. Image Registration. Input Images. Output SR Image (SR =2). Color Image Experiments.

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Image Enhancement

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  1. Image Enhancement Author: Ying Qu, Michael Vaughan Adviser: Professor Mongi A. Abidi IRIS Lab June 8, 2015

  2. Process Pipeline Image Super Resolution Image Blind Deconvolution Image Registration Input Images Output SR Image (SR =2)

  3. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Bicubic Interpolation Input 8 low resolution images of 296×192×3

  4. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Iterated Back Projection(Irani and Peleg 1991) Input 8 low resolution images of 296×192×3

  5. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Robust Super Resolution, Rav-Acha et al. 2001) Input 8 low resolution images of 296×192×3

  6. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using POCS Input 8 low resolution images of 296×192×3

  7. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Papoulis-Gerchberg(Gerchberg 1974) Input 8 low resolution images of 296×192×3

  8. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Normalized Convolution(Pham, Vliet et al. 2006) Input 8 low resolution images of 296×192×3

  9. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using MBD (Sroubek and Flusser 2003) Input 8 low resolution images of 296×192×3

  10. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Fast Direct Single SR(Yang and Yang 2013) Input 8 low resolution images of 296×192×3

  11. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Zero Padding Input 8 low resolution images of 296×192×3

  12. Color Image Experiments (a) Input defocused image (b) Output using our deblurring algorithm

  13. Color Image Experiments (a) Input defocused image (b) Output using our deblurring algorithm

  14. Color Image Experiments (a) Input defocused image (b) Output using our deblurring algorithm

  15. Color Image Experiments (a) Input defocused image (b) Output using our deblurring algorithm

  16. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image 592×384 using Bicubic Interpolation Input 8 low resolution images of 296×192

  17. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image592×384 using Iterated Back Projection(Irani and Peleg 1991) Input 8 low resolution images of 296×192

  18. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image592×384 using Robust Super Resolution(Zomet, Rav-Acha et al. 2001) Input 8 low resolution images of 296×192

  19. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image592×384 using POCS Input 8 low resolution images of 296×192

  20. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image592×384 using Papoulis-Gerchberg(Gerchberg 1974) Input 8 low resolution images of 296×192

  21. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image592×384 using Normalized Convolution(Pham, Vliet et al. 2006) Input 8 low resolution images of 296×192

  22. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image592×384 using Fast Direct Single SR(Yang and Yang 2013) Input 8 low resolution images of 296×192

  23. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image592×384 using Fast Direct Single SR(Yang and Yang 2013) improve contrast. Input 8 low resolution images of 296×192

  24. Black and White Image Experiments Input low resolution image296×192 (b) Output high resolution image592×384 using Zero Padding Input 8 low resolution images of 296×192

  25. Black and White Image Experiments (a) Input defocused image (b) Output using our deblurring algorithm

  26. Black and White Image Experiments (a) Input defocused image (b) Output using our deblurring algorithm

  27. Black and White Image Experiments (a) Input defocused image (b) Output using our deblurring algorithm

  28. Black and White Image Experiments (a) Input defocused image (b) Output using our deblurring algorithm

  29. Efficiency Test For image size 296×192 running on Intel Xeon CPU: 2.13×2, RAM: 12GB • Bi-cubic Interpolation in Matlab code by LCAV: 7.8 s • Iterated Back Projection in Matlab code by LCAV : 33.71 s • Papoulis-Gerchberg in Matlab code by LCAV : 3.5 s • Robust SR in Matlab code by LCAV : 6.91 s • POCS in Matlab code by LCAV : 5.87 s • Normalized Convolution in LCAV code by others: 104.58 s • MBD in Matlab P code by (Sroubek and Flusser 2003): 720 s • Fast Single Image SR in Matlab code by (Yang and Yang 2013): 7.33 s • Zero Padding C++ by IRIS: 0.29 s • De-blurring in Matlab by IRIS: 32 s for image size 592×384

  30. Process Pipeline Image Super Resolution Image Blind Deconvolution Image Registration Input Images Output SR Image (SR =2)

  31. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Normalized Convolution(Pham, Vliet et al. 2006) after deblurring Input 8 low resolution images of 296×192×3

  32. Color Image Experiments Input low resolution image296×192×3 (b) Output high resolution image592×384 using Normalized Convolution(Pham, Vliet et al. 2006) Input 8 low resolution images of 296×192×3

  33. Color Image Experiments (c) Deblurring+ Normalized Convolution (b) Normalized Convolution +Deblurring (a) Normalized Convolution

  34. Conclusion Super Resolution Step • The best result got from the six multi-image super resolution algorithms is the normalized convolution from (Pham, Vliet et al. 2006) and MBD from (Šroubek and Flusser 2003), but Šroubek also scale the intensity of the image. Single image Super Resolution method based on training data (Yang and Yang 2013) is able to recover some details of the images. Blind Deconvolution Step • Blind Deconvolution can reduce the blurriness of the super resolution images. • Applying blind deconvolution before super resolution will sharper the output images and reduce the artifacts.

  35. Reference Yang, C.-Y. and M.-H. Yang (2013). Fast direct super-resolution by simple functions. Computer Vision (ICCV), 2013 IEEE International Conference on, IEEE. Irani, M. and S. Peleg (1991). "Improving resolution by image registration." CVGIP: Graph. Models Image Process.53(3): 231-239. Zomet, A., et al. (2001). Robust super-resolution. Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on. Gerchberg, R. W. (1974). "Super-resolution through Error Energy Reduction." OpticaActa: International Journal of Optics21(9): 709-720. Pham, T. Q., et al. (2006). "Robust fusion of irregularly sampled data using adaptive normalized convolution." EURASIP J. Appl. Signal Process.2006: 236-236. Sroubek, F. and J. Flusser (2003). "Multichannel blind iterative image restoration." Image Processing, IEEE Transactions on12(9): 1094-1106.

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