Testing Gaussian process with applications to super-resolution
نویسندگان
چکیده
منابع مشابه
Single Image Super-Resolution using Gaussian Process Regression
In this paper we address the problem of producing a high-resolution image from a single low-resolution image without any external training set. We propose a framework for both magnification and deblurring using only the original low-resolution image and its blurred version. In our method, each pixel is predicted by its neighbors through the Gaussian process regression. We show that when using a...
متن کاملMulti-task Gaussian Process Regression-based Image Super Resolution
Image super resolution (SR) aims at recovering the missing high frequency details from single image or multiple images. Existing SR methods can be divided into three categories: interpolation-based, reconstructionbased and example learning-based. Our paper focuses on the third category. Example learning-based SRmethods [6] utilize the LR-HR image pair to infer the missing high-frequency details...
متن کاملLearning local Gaussian process regression for image super-resolution
Learning based super-resolution (SR) methods, which predict the high-resolution pixel values but not directly provide an estimation of uncertainty, are typically non-probabilistic and have limited generalization ability. Gaussian processes can provide a framework for deriving regression techniques with explicit uncertainty models, but Gaussian Process Regression (GPR) has a significant drawback...
متن کاملEfficient Super-Resolution and Applications to Mosaics
Mosaicing and super resolution are two ways to combine information from multiple frames in video sequences. Mosaicing displays the information of multiple frames in a single panoramic image. Super-resolution uses regions which appear in multiple frames to improve resolution and reduce noise. The aim of this work is constructing a high resolution mosaic from a video sequence in an efficient way....
متن کاملGaussian Process Conditional Copulas with Applications to Financial Time Series
The estimation of dependencies between multiple variables is a central problem in the analysis of financial time series. A common approach is to express these dependencies in terms of a copula function. Typically the copula function is assumed to be constant but this may be inaccurate when there are covariates that could have a large influence on the dependence structure of the data. To account...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied and Computational Harmonic Analysis
سال: 2020
ISSN: 1063-5203
DOI: 10.1016/j.acha.2018.07.001