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Kernel Estimation Using Total Variation Guided GAN for Image Super-Resolution [PDF]
Sensors, 2023 Various super-resolution (SR) kernels in the degradation model deteriorate the performance of the SR algorithms, showing unpleasant artifacts in the output images. Hence, SR kernel estimation has been studied to improve the SR performance in several ways
Jongeun Park, Hansol Kim, Moon Gi Kang
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Contingent kernel density estimation. [PDF]
PLoS ONE, 2012 Kernel density estimation is a widely used method for estimating a distribution based on a sample of points drawn from that distribution. Generally, in practice some form of error contaminates the sample of observed points.
Scott Fortmann-Roe +2 more
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Kernel density estimation and its application
ITM Web of Conferences, 2018 Kernel density estimation is a technique for estimation of probability density function that is a must-have enabling the user to better analyse the studied probability distribution than when using a traditional histogram. Unlike the histogram, the kernel
Węglarczyk Stanisław
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Regularized nonparametric Volterra kernel estimation [PDF]
Automatica, 2017 In this paper, the regularization approach introduced recently for nonparametric estimation of linear systems is extended to the estimation of nonlinear systems modelled as Volterra series. The kernels of order higher than one, representing higher dimensional impulse responses in the series, are considered to be realizations of multidimensional ...
Georgios Birpoutsoukis +3 more
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Variational Dirichlet Blur Kernel Estimation
IEEE Transactions on Image Processing, 2015 Blind image deconvolution involves two key objectives: 1) latent image and 2) blur estimation. For latent image estimation, we propose a fast deconvolution algorithm, which uses an image prior of nondimensional Gaussianity measure to enforce sparsity and an undetermined boundary condition methodology to reduce boundary artifacts. For blur estimation, a
Xu, Zhou +4 more
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Mutual Affine Network for Spatially Variant Kernel Estimation in Blind Image Super-Resolution [PDF]
IEEE International Conference on Computer Vision, 2021 Existing blind image super-resolution (SR) methods mostly assume blur kernels are spatially invariant across the whole image. However, such an assumption is rarely applicable for real images whose blur kernels are usually spatially variant due to factors
Jingyun Liang +4 more
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Unfolded Deep Kernel Estimation for Blind Image Super-resolution [PDF]
European Conference on Computer Vision, 2022 Blind image super-resolution (BISR) aims to reconstruct a high-resolution image from its low-resolution counterpart degraded by unknown blur kernel and noise.
Hong Zheng, Hongwei Yong, Lei Zhang
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KDEformer: Accelerating Transformers via Kernel Density Estimation [PDF]
International Conference on Machine Learning, 2023 Dot-product attention mechanism plays a crucial role in modern deep architectures (e.g., Transformer) for sequence modeling, however, na\"ive exact computation of this model incurs quadratic time and memory complexities in sequence length, hindering the ...
A. Zandieh +3 more
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Multi-Frame Blind Super-Resolution Based on Joint Motion Estimation and Blur Kernel Estimation
Applied Sciences, 2022 Multi-frame super-resolution makes up for the deficiency of sensor hardware and significantly improves image resolution by using the information of inter-frame and intra-frame images.
Shanshan Liu +2 more
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Statistics & Risk Modeling, 2012 Abstract Many statistical estimation techniques for high-dimensional or functional data are based on a preliminary dimension reduction step, which consists in projecting the sample X 1,...,X n onto the first D eigenvectors of the Principal Component Analysis ...
Biau, Gérard, Mas, André
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