Kernel Regression for Image Processing and Reconstruction

By Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar. IEEE Transactions on Image Processing, 2007

1,445 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/3ff7d797f59971b1df94d14ac8bc931b136f10f2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Mechanics

In Computer Vision and Pattern Recognition

Citations in the corpus

Automatic Parameter Selection for Denoising Algorithms Using a No-Reference Measure of Image Content (2010, background): “effect of these parameters is generally interdependent in that the smaller the is, the more iterations are needed to achieve the best output image [20].”
Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach (2013, methodology): “The experimental setup 462 for image interpolation from irregularly samples is adopted 463 from [46] - we randomly delete 85% pixels from an image 464 and compare the reconstructed images from the remaining 465 15% samples.”
Group-Based Sparse Representation for Image Restoration (2014, methodology): “From left to right and top to bottom: the masked image, original image, the recovered images by SKR [6] (PSNR = 30.”
Sparse Modeling for Image and Vision Processing (2014, methodology): “…video processing [Protter and Elad, 2009], and finally, we conclude this section with a presentation of other patch-modeling approaches [Buades et al., 2005, Awate and Whitaker, 2006, Dabov et al., 2007a, Takeda et al., 2007, Zoran and Weiss, 2011, Yu et al., 2012, Chatterjee and Milanfar, 2012].”
Super-Resolution Without Explicit Subpixel Motion Estimation (2009, methodology): “work [15] which we will review in Section II-A2.”
Super-Resolution With Sparse Mixing Estimators (2010, methodology): “The proposed image zooming algorithm, named hereafter sparse mixing estimation (SME), is compared with linear interpolators [8], [34], [45], [58] as well as recent super-resolution algorithms new edge directed interpolation (NEDI) [37], directional filtering and data fusion (DFDF), kernel regression”
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression (2012, methodology): “Followed by [31], a general representation model for image restoration is given by y i = z (x i ) + η i , i = 1, . . . , M (1) where x i ∈ R 2 denotes the coordinate of the observation y i , z (x i ) is a general regression function and η i is the additive Gaussian noise.”
From Heuristic Optimization to Dictionary Learning: A Review and Comprehensive Comparison of Image Denoising Algorithms (2014, methodology): “As an emerging machine learning technique, sparse representations have become a trend and are used for restoration problems [11], [40], [41].”
Static and space-time visual saliency detection by self-resemblance. (2009, methodology): “For a more in depth analysis of local steering kernels, we refer the interested reader to Takeda et al. (2007) and Takeda et al. (2009).”
Generalizing the Nonlocal-Means to Super-Resolution Reconstruction (2009, background): “For example, the steering kernel may provide an interesting alternative, taking into account the correlation between the pixel positions and their value [34].”