Cubic convolution interpolation for digital image processing

By R. Keys. 1981

Subfields: Computer Vision and Pattern Recognition

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Citations in the corpus

On Buggy Resizing Libraries and Surprising Subtleties in FID Calculation (2021, methodology): “The bilinear filter is a triangular filter, bicubic [39] is a stronger cubic function, and the lanczos filter is an enveloped sinc.”
Kernel Interpolation for Scalable Structured Gaussian Processes (KISS-GP) (2015, methodology): “For greater accuracy, we can use local cubic interpolation (Keys, 1981) on equispaced grids, in which case W has c = 4 non-zero entries per row.”
Thoughts on Massively Scalable Gaussian Processes (2015, methodology): “For example, if we are performing local cubic (Keys, 1981) interpolation for d -dimensional input data, W X and W Z contain only 4 d non-zero entries per row.”
Millimeter Wave Beam-Selection Using Out-of-Band Spatial Information (2017, unknown): “blers for the Twenty-twenty Information Society, Tech. Rep., 2015. [50] A. M. Sayeed, ÒDeconstructing multiantenna fading channels,Ó IEEE Trans. Signal Process., vol. 50, no. 10, pp. 2563Ð2579, 2002. [51] A. Alkhateeb, G. Leus, and R. W. Heath Jr., ÒCompressed sensing based multi-user millimeter wav”
Scalable Gaussian Processes with Billions of Inducing Inputs via Tensor Train Decomposition (2017, methodology): “Authors of KISS-GP suggest using cubic convolutional interpolation (Keys (1981)), in which case the interpolation weights wi can be represented as a Kronecker product over dimensions”
Deep Learning for Image Super-Resolution: A Survey (2019, methodology): “Similarly, the bicubic interpolation (BCI) [10] performs cubic interpolation on each of the two axes, as Fig.”
Learning Enriched Features for Fast Image Restoration and Enhancement (2022, methodology): “Prior to the deep-learning era, numerous super-resolution (SR) algorithms have been proposed based on the sampling theory [45], [46], edge-guided interpolation [47], [48], natu-ral image priors [49], [50], patch-exemplars [51], [52] and sparse representations [53], [54].”
Deep Learning for Single Image Super-Resolution: A Brief Review (2018, methodology): “Interpolation-based SISR methods, such as bicubic interpolation [10] and Lanczos resampling [11], are very speedy and straightforward but suffer from accuracy shortcomings.”
Ncorr: Open-Source 2D Digital Image Correlation Matlab Software (2015, background): “bicubic keys [50]), and thus must be solved for directly.”
State Space Gaussian Processes with Non-Gaussian Likelihood (2018, methodology): “Since the matrix exponential ψ : s 7→ e is smooth, its evaluation can be accurately approximated by convolution interpolation (Keys, 1981) as done for the covariance functions in the KISS-GP framework (Wilson & Nickisch, 2015; Wilson et al.”
A Deep Convolutional Neural Network for segmenting and classifying epithelial and stromal regions in histopathological images (2016, methodology): “Each tissue compartment is subsequently resized into 50 50 square sub-image patches via a bicubic interpolation approach [16].”
Convex Optimization in Signal Processing and Communications (2009, methodology): “These include nearest neighbor (B-spline of order 0), bilinear (B-spline of order 1), bicubic (Keys kernel [35]), Lanczos and B-spline of order 3.”
Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation (2008, background): “2(a) illustrates the spatial configuration of known and missing pixels involved in the first pass.”
A Quantitative Analysis of Current Practices in Optical Flow Estimation and the Principles Behind Them (2013, methodology): “Because the Matlab built-in function interp2 is based on cubic convolution (Keys 1981) and does not provide the derivatives used in interpolation, we use the spline-based implementation by Press et al. (2002).”
Secrets of optical flow estimation and their principles (2010, methodology): “The MATLAB built-in function interp2 is based on cubic convolution approximation [20].”
Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis (2012, methodology): “We consider the second case in this paper, and the super-resolution problem can be viewed as an image interpolation problem [12, 29, 15, 21].”
An edge-guided image interpolation algorithm via directional filtering and data fusion (2006, methodology): “A number of image interpolation methods have been developed [1], [2], [5], [6], [8]–[16].”