Write in a clean editor, then format for SIAM Journal on Imaging Sciences in one click — DocuGuru applies the official SIAM template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
SIAM Journal on Imaging Sciences is a peer-reviewed journal published by SIAM, covering Sparse and Compressive Sensing Techniques, Image and Signal Denoising Methods, Numerical methods in inverse problems. Over its lifetime it has published 1,298 papers, which have received 65,653 citations.
| Publisher | SIAM |
|---|---|
| Reference style | Numbered (SIAM) Numbered — [1], [2] in the text [1] A. Smith, B. Jones, and C. Lee, A representative article title, SIAM Journal on Imaging Sciences 12 (2023) 45–58.
Formats any DOI in the closest standard style — SIAM Journal on Imaging Sciences has no published style definition, so this is an approximation. No sign-up. |
| Publishes research in | Sparse and Compressive Sensing Techniques Image and Signal Denoising Methods Numerical methods in inverse problems Medical Image Segmentation Techniques Advanced Image Processing Techniques |
| ISSN | 1936-4954 |
| Citation impact (2-yr) | 2.32 |
| h-index | 102 |
| i10-index | 667 |
| Top institutions publishing here | Centre National de la Recherche Scientifique |
| Journal website | epubs.siam.org |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Citations each year’s papers have accumulated so far — the most recent years are still building up.
We consider the class of iterative shrinkage-thresholding algorithms (ISTA) for solving linear inverse problems arising in signal/image processing. This class of methods, which can be viewed as an extension of the classical gradient algorithm, is attractive due to its simplicity and thus is adequate for solving large-scale problems even with dense matrix data. However, such…
The class of L1-regularized optimization problems has received much attention recently because of the introduction of “compressed sensing,” which allows images and signals to be reconstructed from small amounts of data. Despite this recent attention, many L1-regularized problems still remain difficult to solve, or require techniques that are very problem-specific. In this paper, we show…
We propose, analyze, and test an alternating minimization algorithm for recovering images from blurry and noisy observations with total variation (TV) regularization. This algorithm arises from a new half-quadratic model applicable to not only the anisotropic but also the isotropic forms of TV discretizations. The per-iteration computational complexity of the algorithm is three fast Fourier…
The novel concept of total generalized variation of a function u is introduced and some of its essential properties are proved. Differently from the bounded variation semi-norm, the new concept involves higher order derivatives of u. Numerical examples illustrate the high quality of this functional as a regularization term for mathematical imaging problems. In particular…
If a physical object has a smooth or piecewise smooth boundary, its images obtained by cameras in varying positions undergo smooth apparent deformations. These deformations are locally well approximated by affine transforms of the image plane. In consequence the solid object recognition problem has often been led back to the computation of affine invariant image…
SIAM Journal on Discrete Mathematics
SIAM Journal on Life Sciences
SIAM Journal on Mathematics of Data Science
SIAM Journal on Numerical Analysis
SIAM Journal on Scientific Computing
SIAM/ASA Journal on Uncertainty Quantification
Journal of the American Mathematical Society
Proceedings of the American Mathematical Society
