Results 71 to 80 of about 19,283,190 (228)
On the Equivalence of Soft Wavelet Shrinkage, Total Variation Diffusion, Total Variation Regularization, and SIDEs [PDF]
SIAM Journal on Numerical Analysis, 2004 Summary: Soft wavelet shrinkage, total variation (TV) diffusion, TV regularization, and a dynamical system called SIDEs are four useful techniques for discontinuity preserving denoising of signals and images. In this paper we investigate under which circumstances these methods are equivalent in the one-dimensional case.
Gabriele Steidl +4 more
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Applied Sciences, 2019 The limitations of hyperspectral sensors usually lead to coarse spatial resolution of acquired images. A well-known fusion method called coupled non-negative matrix factorization (CNMF) often amounts to an ill-posed inverse problem with poor anti-noise ...
Fei Ma +3 more
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Comparative Analysis of Digital Elevation Model Generation Methods Based on Sparse Modeling
Remote Sensing, 2023 With the spread of aerial laser bathymetry (ALB), seafloor topographies are being measured more frequently. Nevertheless, data deficiencies occur owing to seawater conditions and other factors. Conventional interpolation methods generally need to produce
Takashi Fuse, Kazuki Imose
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A tool in establishing total variation convergence [PDF]
Proceedings of the American Mathematical Society, 1985 Let X 0 , X 1 , X 2 , … and Y 0 , Y 1
PARTHASARATHY, KR, STEERNEMAN, T
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Difference of anisotropic and isotropic TV for segmentation under blur and Poisson noise
Frontiers in Computer Science, 2023 In this paper, we aim to segment an image degraded by blur and Poisson noise. We adopt a smoothing-and-thresholding (SaT) segmentation framework that finds a piecewise-smooth solution, followed by k-means clustering to segment the image. Specifically for
Kevin Bui +3 more
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Blind Image Deblurring via Reweighted Graph Total Variation
, 2017 Blind image deblurring, i.e., deblurring without knowledge of the blur kernel, is a highly ill-posed problem. The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, de-convolve blurry
Bai, Yuanchao +3 more
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Prediction bounds for higher order total variation regularized least squares
, 2020 We establish adaptive results for trend filtering: least squares estimation with a penalty on the total variation of $(k-1)^{\rm th}$ order differences.
Ortelli, Francesco, van de Geer, Sara
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Wasserstein total variation filtering
CoRR, 2019 In this paper, we expand upon the theory of trend filtering by introducing the use of the Wasserstein metric as a means to control the amount of spatiotemporal variation in filtered time series data. While trend filtering utilizes regularization to produce signal estimates that are piecewise linear, in the case of $\ell_1$ regularization, or temporally
Erdem Varol, Amin Nejatbakhsh
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Remote Sensing, 2014 Multi-angle remote sensing images are acquired over the same imaging scene from different angles, and share similar but not identical information. It is therefore possible to enhance the spatial resolution of the multi-angle remote sensing images by the
Hongyan Zhang +3 more
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Computers and Mathematics with Applications, 2019 In this paper, we propose a novel model to restore an image corrupted by blur and Cauchy noise. The model is composed of a data fidelity term and two regularization terms including total variation and high-order total variation.
Jinghua Yang +5 more
semanticscholar +1 more source