Image Interpolation by Adaptive 2-D Autoregressive Modeling and Soft-Decision Estimation

By Xiangjun Zhang, Xiaolin Wu. IEEE Transactions on Image Processing, 2008

628 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/12502e0db30131d162ae4164aa286c63b0c7061e

How researchers see this paper

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

In Computer Vision and Pattern Recognition

Citations in the corpus

Nonlocal Image Restoration With Bilateral Variance Estimation: A Low-Rank Approach (2013, methodology): “473 Next, we compare SAIST-based image interpolation and 474 three other leading methods (bicubic, NEDI [45] and SAI [50]) 475 for regularly-decimated samples.”
Solving Inverse Problems With Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity (2010, methodology): “I NTERPOLATION ZOOMING Interpolation zooming is a special case of inpainting with regular subsampling on uniform grids.”
On Single Image Scale-Up Using Sparse-Representations (2010, result): “is the approach taken, for example, in [18,19], and this explains why we cannot compare our results to theirs.”
Super-Resolution With Sparse Mixing Estimators (2010, methodology): “Table I shows that a contourlet algorithm [45] sometimes can provide a slightly better PSNR then a bicubic interpolation, but these results are below th e s ate of the art obtained with adaptive directional interpolators [63].”
Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis (2012, methodology): “3 we can see that although the SAI method can preserve well the image edges, it will also over-smooth the edges to some extent.”
Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model (2014, methodology): “The AR model has been applied in many image processing applications, such as detecting and interpolating missing areas in image sequences [46], super-resolution, forecasting of spatial-temporal data [47], [48], as well as backward adaptive video coding [49].”
A Statistical Prediction Model Based on Sparse Representations for Single Image Super-Resolution (2014, background): “Models that are commonly exploited in single image SR methods include image smoothness and geometric regularity of image structures [3]), gradient profile priors [4], self-similarity of image patches within and across different scales in the same image [6], [7], [24], and sparsity – either in the wa”
Gradient Profile Prior and Its Applications in Image Super-Resolution and Enhancement (2011, neutral): “D autoregressive modeling [26].”