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Hiro (Hiroyuki Takeda) Ph.D. |
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I received the B.S. degree in electronics from Kinki University, Japan, and the M.S. degree in electrical engineering from the University of California, Santa Cruz (UCSC), in 2001 and 2006, respectively. I am currently pursuing the Ph.D. degree in electrical engineering at UCSC under the supervision of Prof Peyman Milanfar. My research interests are in image processing (motion estimation, interpolation, and super-resolution) and inverse problems.
The principal purpose on my research is to produce a superior quality image from an image or several images in low quality by software, instead of implementing expensive and high performance hardware. Nowadays, digital imaging systems are widely used; personal use digital camera, astronomical telescope, medical imaging systems (e.g. CT-Scan, MRI), and so on. Since images given by a CCD array sensor of the systems are often noisy, blurry, and aliased, the necessity of image processing to recover an unknown real scene is getting higher..
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Takeda, H., S. Farsiu, and P. Milanfar, "Kernel Regression for Image Processing and Reconstruction", IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 349-366, February 2007.
- Takeda, H., S.
farsiu, and P. Milanfar, "Deblurring
Using Regularized Locally-Adaptive Kernel Regression",
IEEE Transactions on Image Processing, Vol. 17, No. 4, pp. 550-563,
April 2008.
- Protter, M., M.
Elad, H. Takeda, and P. Milanfar, "Generalizing
the Non-Local-Means to Super-Resolution Reconstruction",
IEEE Transactions on Image Processing, Vol. 16, No. 2, pp. 36-51, January
2009.
- Takeda, H., P. Milanfar, M. Protter, and M. Elad, "Superresolution without Explicit Subpixel Motion Estimation", IEEE Transactions on Image Processing, Vol. 18, No. 9, September 2009.
Conference Paper:
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Takeda, H., and P. Milanfar, "Nonlinear Kernel Backprojection for Computed Tomography", To appear in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Dallas, TX, March, 2010.
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Takeda, H., and P.
Milanfar, "An
Adaptive Nonparametric Approach to Restoration and Interpolation for
Medical Imaging", Proceedings of IEEE International
Symposium on Biomedical Imaging (ISBI): From Nano to Macro, Boston,
MA, June, 2009.
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Takeda, H., P van Beek, and P. Milanfar, "Spatio-Temporal Video Interpolation and Denoising Using Motion-Assisted Steering Kernel (MASK) Regression", Proceedings of IEEE International Conference on Image Processing (ICIP), San Diego, CA, October 2008.
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Takeda, H., HaeJong Seo, and P. Milanfar, "Statistical Approaches to Quality Assessment for Image Restoration", Proceedings of International Conference on Consumer Electronics (ICCE), Las Vegas, NV, January 2008.
- Seo, HaeJong.,,
P. Chatterjee, H. Takeda, and P. Milanfar, "A
Comparison of Some State of the Art Image Denoising Methods",
Proceedings of the 41st Asilomar Conference on Signals, Systems, and
Computers, 2007
- Takeda, H., S.
Farsiu, and P. Milanfar, "Higher
Order Bilateral Filters and Their Properties", Proceedings
of the SPIE Conf. on Computational Imaging, San Jose, January 2007."
- Takeda, H., S.
Farsiu, and P. Milanfar, "Regularized
Kernel Regression for Image Deblurring", Proceedings
of the 40th Asilomar Conference on Signals, Systems, and Computers,
Pacific Grove, CA, November 2006.
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Takeda, H., S. Farsiu, and P. Milanfar, "Robust Kernel Regression for Restoration and Reconstruction of Images from Sparse Noisy Data", Proceedings of the International Conference on Image Processing (ICIP), Atlanta, GA, October 2006.
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Takeda, H., S. Farsiu, J. Christou, P. Milanfar, "Super-Drizzle: Applications of Adaptive Kernel Regression in Astronomical Imaging", Advanced Maui Optical and Space Surveillance (AMOS) Technologies Conference, September 2006.
- Takeda, H., S. Farsiu, and P. Milanfar, "Image Denoising by Adaptive Kernel Regression", Proceedings of the 39th Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, November 2005.
Book Chapter:
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H. Takeda, P. van Beek, and P. Milanfar, "Spatiotemporal Video Upscaling using Motion-Assisted Steering Kernel (MASK) Regression", to appear as a chapter in High-quality visual experience: creation, processing and interactivity of high-resolution and high-dimensional video signals, Springer-Verlag, 2010
- H. Takeda and P. Milanfar,"Locally Adaptive Kernel Regression for Space-Time Super-Resolution", To appear as a Chapter in Super-resolution Imaging Edited by P. Milanfar, Available September 27, 2010.
Dissertation:
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Takeda, H., M.S. Thesis, "Kernel Regression for Image Processing and Reconstruction", Electrical Engineering, UC Santa Cruz, March 2006.
- Hiroyuki Takeda, Ph.D. Thesis, "Locally Adaptive Kernel Regression Methods for Multi-dimensional Signal Processing", Electrical Engineering, UC Santa Cruz, September 2010.