Title :
Kernel Regression for Image Processing and Reconstruction
Author :
Takeda, Hiroyuki ; Farsiu, Sina ; Milanfar, Peyman
Author_Institution :
Electr. Eng. Dept., Univ. of California, Santa Cruz, CA
Abstract :
In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples
Keywords :
filtering theory; image denoising; image fusion; image reconstruction; interpolation; statistics; bilateral filter; image denoising; image fusion; image interpolation; image processing; image reconstruction; image upscaling; kernel regression; nonparametric statistics; Charge coupled devices; Costs; Digital images; Filters; Image processing; Image reconstruction; Interpolation; Kernel; Noise reduction; Spatial resolution; Bilateral filter; denoising; fusion; interpolation; irregularly sampled data; kernel function; kernel regression; local polynomial; nonlinear filter; nonparametric; scaling; spatially adaptive; super-resolution; Algorithms; Artificial Intelligence; Image Enhancement; Image Interpretation, Computer-Assisted; Information Storage and Retrieval; Regression Analysis; Signal Processing, Computer-Assisted;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2006.888330
