Generalizing the Nonlocal-Means to Super-Resolution Reconstruction

By Matan Protter, Michael Elad, Hiroyuki Takeda, Peyman Milanfar. IEEE Transactions on Image Processing, 2009

750 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/64ba0daedb8be4d802339d7dab89496d28d973bc

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Citations in the corpus

Seven Ways to Improve Example-Based Single Image Super Resolution (2015, methodology): “To address this problem, the SR literature proposes interpolation-based meth-ods [29], reconstruction-based methods [3, 15, 25, 39, 41], and learning-based methods [18, 10, 30, 31, 7, 5, 42, 6].”
Sparse Representation Based Image Interpolation With Nonlocal Autoregressive Modeling (2013, methodology): “Such kind of nonlocal similarity is very helpful to improve the image restoration output, and it has been successfully used in image denoising [31-34], deblurring [35-36] and super-resolution [37-38, 49].”
Robust Super-resolution Volume Reconstruction from Slice Acquisitions: Application to Fetal Brain MRI (2010, background): “Also, a few groups have recently developed super-resolution reconstruction techniques that are robust to motion estimation errors [27], [28].”
Bidirectional Recurrent Convolutional Networks for Multi-Frame Super-Resolution (2015, methodology): “To overcome these problems, several solutions have been explored by avoiding the explicit motion estimation [35], [42].”
Image Deblurring and Super-Resolution by Adaptive Sparse Domain Selection and Adaptive Regularization (2010, methodology): “This NL self-similarity prior was later employed in image deblurring [8], [20] and super-resolution [41].”
Super-resolution: a comprehensive survey (2014, methodology): “TV [73], [84], [124], [157], [245], [291], [307], [313], [314], [331], [353], [388], [414], [418], [419], [456] , [506], [512], [554], [581], [596], [606]”
Super-resolution from a single image (2009, methodology): “Our approach attempts to recover at each pixel its best possible resolution increase based on its patch redundancy within and across scales.”
Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation (2011, background): “…patch-based methods are currently the focus of attention of the computer vision community in various domains such as texture synthesis (Efros and Freeman, 2001), in-painting (Criminisi et al., 2004), restoration (Buades et al., 2005), and single-frame super resolution (Protter et al., 2009).”
Super-Resolution Without Explicit Subpixel Motion Estimation (2009, methodology): “Vdeo upscaling example using Foreman sequence: (a) the upscaled frames by NL-means based SR [19] [PSNR: 32.”
Multilevel and Multiscale Network for Single-Image Super-Resolution (2019, neutral): “2952047 reconstruction-based methods [7] and so on.”
Super-Resolution With Sparse Mixing Estimators (2010, methodology): “Super-resolution interpolations can further be improved by using a sequence of images [26], [29], [30], [46], [48] or a comparison dataset [21], [27], [59] to perform the interpolation.”
Accurate Blur Models vs. Image Priors in Single Image Super-resolution (2013, background): “A few single-image SR works which do attempt to estimate or take the unknown kernel into account include [3, 24, 17, 10, 11, 12].”
Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression (2012, methodology): “Both the NLM [11] and SKR regression [31] can achieve better results than the bicubic interpolation.”