An Improved FoE Model for Image Deblurring
International Journal of Computer Vision
Clustering-based denoising with locally learned dictionaries
IEEE Transactions on Image Processing
Super-resolution without explicit subpixel motion estimation
IEEE Transactions on Image Processing
An adaptive nonparametric approach to restoration and interpolation for medical imaging
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
IEEE Transactions on Image Processing
Efficient Fourier-wavelet super-resolution
IEEE Transactions on Image Processing - Special section on distributed camera networks: sensing, processing, communication, and implementation
Journal of Mathematical Imaging and Vision
High-quality non-blind image deconvolution with adaptive regularization
Journal of Visual Communication and Image Representation
Depth map enhancement using adaptive steering Kernel regression based on distance transform
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part I
Contour Stencils: Total Variation along Curves for Adaptive Image Interpolation
SIAM Journal on Imaging Sciences
Image deblurring with matrix regression and gradient evolution
Pattern Recognition
A New TV-Stokes Model with Augmented Lagrangian Method for Image Denoising and Deconvolution
Journal of Scientific Computing
Image deconvolution using incomplete Fourier measurements
International Journal of Imaging Systems and Technology
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Kernel regression is an effective tool for a variety of image processing tasks such as denoising and interpolation . In this paper, we extend the use of kernel regression for deblurring applications. In some earlier examples in the literature, such nonparametric deblurring was suboptimally performed in two sequential steps, namely denoising followed by deblurring. In contrast, our optimal solution jointly denoises and deblurs images. The proposed algorithm takes advantage of an effective and novel image prior that generalizes some of the most popular regularization techniques in the literature. Experimental results demonstrate the effectiveness of our method.