Summary
Kernel Regression-Based Image Processing ToolBox is a command-line based software package for MATLAB, which was developed at MDSP research laboratory in UCSC. The package is capable of performing several general image processing tasks; for instance,
-
Image denoising
- Gaussian noise removal
- Compression artifact reduction
- Film grain reduction
- Salt & pepper noise reduction
-
Image reconstruction (interpolation + denoising)
- Image upscale (reconstruction an image from regularly sampled data set)
- Image reconstruction from an irregularly sampled data set
- Image fusion
- Super-resolution
We are demonstrating examples in here.
Relevant publications
- 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, "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.
- Hiroyuki Takeda,
Ph.D. Thesis, "Locally
Adaptive Kernel Regression Methods for Multi-dimensional Signal Processing",
Electrical Engineering, UC Santa Cruz, September 2010.
Download
The latest version of the software package (ver. 1.2 beta) can be downloaded from here.
This is experimental software. It is provided for noncommercial research purposes only. Use at your own risk. No warranty is implied by this distribution. Copyright © 2007 by University of California.
User's manual
Acknowledgements
This work was supported in part by the US Air Force Grant F49620-03-1-0387.