A new PDE learning model for image denoising
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2022/03/07
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Indian UGC (journal)
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Refrences37
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Citations5
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F. Ashouri
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M. R. Eslahchi ORCID (unauthenticated)
Cite
Ashouri, F., and M. R. Eslahchi. “A New PDE Learning Model for Image Denoising”. Neural Computing and Applications, vol. 34, no. 11, 2022, pp. 8551-74, https://doi.org/10.1007/s00521-021-06620-4.
Ashouri, F., & Eslahchi, M. R. (2022). A new PDE learning model for image denoising. Neural Computing and Applications, 34(11), 8551-8574. https://doi.org/10.1007/s00521-021-06620-4
Ashouri F, Eslahchi MR. A new PDE learning model for image denoising. Neural Computing and Applications. 2022;34(11):8551-74.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Technology
Engineering (General)
Civil engineering (General)
Technology
Mechanical engineering and machinery
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Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Learning sparse partial differential equations for vector-valued images | Neural Computing and Applications |
| 4 | 2018 |
| Feature learning via partial differential equation with applications to face recognition | Pattern Recognition |
| 15 | 2017 |
| Optimized first-order methods for smooth convex minimization | Mathematical Programming |
| 68 | 2016 |
| Toward designing intelligent PDEs for computer vision: An optimal control approach | Image and Vision Computing |
| 15 | 2013 |
| Shock coupled fourth-order diffusion for image enhancement | Computers and Electrical Engineering |
| 9 | 2012 |
Refrences Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 17
is the most frequently represented among the references in this article. It primarily includes studies from IEEE Transactions on Image Processing The chart below illustrates the number of referenced publications per year.
Refrences used by this article by year
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| NODE-ImgNet: A PDE-informed effective and robust model for image denoising | Pattern Recognition |
| 2024 | |
| The use of physics-informed neural network approach to image restoration via nonlinear PDE tools | Computers & Mathematics with Applications |
| 2023 | |
SINGLE-VALUED NEUTROSOPHIC SET WITH QUATERNION INFORMATION: A PROMISING APPROACH TO ASSESS IMAGE QUALITY | Fractals |
| 2023 | |
| NODE-Imgnet: A PDE-Informed Effective and Robust Model for Image Denoising | SSRN Electronic Journal | 2023 | ||
A Novel Merging Method for Generating High-Quality Spatial Precipitation Information over Mainland China | Water |
| 2023 |
Citations Analysis
The category
Science: Mathematics 2
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled The use of physics-informed neural network approach to image restoration via nonlinear PDE tools and was published in 2023. The most recent citation comes from a 2024 study titled NODE-ImgNet: A PDE-informed effective and robust model for image denoising. This article reached its peak citation in 2023, with 4 citations. It has been cited in 5 different journals. Among related journals, the Pattern Recognition cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.