Speckle noise removal via learned variational models

Article Properties
Cite
Cuomo, Salvatore, et al. “Speckle Noise Removal via Learned Variational Models”. Applied Numerical Mathematics, vol. 200, pp. 162-78, https://doi.org/10.1016/j.apnum.2023.06.002.
Cuomo, S., De Rosa, M., Izzo, S., Piccialli, F., & Pragliola, M. (n.d.). Speckle noise removal via learned variational models. Applied Numerical Mathematics, 200, 162-178. https://doi.org/10.1016/j.apnum.2023.06.002
Cuomo, Salvatore, Mariapia De Rosa, Stefano Izzo, Francesco Piccialli, and Monica Pragliola. “Speckle Noise Removal via Learned Variational Models”. Applied Numerical Mathematics 200 (n.d.): 162-78. https://doi.org/10.1016/j.apnum.2023.06.002.
1.
Cuomo S, De Rosa M, Izzo S, Piccialli F, Pragliola M. Speckle noise removal via learned variational models. Applied Numerical Mathematics. 200:162-78.
Journal Categories
Science
Mathematics
Technology
Technology (General)
Industrial engineering
Management engineering
Applied mathematics
Quantitative methods
You May Also Like
Refrences
Title Journal Journal Categories Citations Publication Date
Multiplicative noise removal for texture images based on adaptive anisotropic fractional diffusion equations SIAM Journal on Imaging Sciences
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods
  • Technology: Photography
  • Technology: Mechanical engineering and machinery
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Mathematics
 2019
Image denoising method based on a deep convolution neural network IET Image Processing
  • Technology: Photography
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software
  • Science: Mathematics: Instruments and machines: Electronic computers. Computer science
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Photography
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 60 2018
SAR image despeckling using a convolutional neural network IEEE Signal Processing Letters
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Engineering (General). Civil engineering (General)
 2017
Constrained TV $$_p$$ p - $$\ell _2$$ ℓ 2 Model for Image Restoration Journal of Scientific Computing
  • Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods
  • Science: Mathematics
 45 2016
A nonlocal SAR image denoising algorithm based on LLMMSE wavelet shrinkage IEEE Transactions on Geoscience and Remote Sensing
  • Science: Geology
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
  • Geography. Anthropology. Recreation: Geography (General)
  • Technology: Photography
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Science: Geology
 2012
Refrences Analysis
Category Category Repetition
Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods9
Science: Mathematics9
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics9
Science: Mathematics: Instruments and machines: Electronic computers. Computer science5
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks5
Science: Geology5
Technology: Photography4
Science: Mathematics: Instruments and machines: Electronic computers. Computer science: Computer software3
Technology: Mechanical engineering and machinery3
Technology: Engineering (General). Civil engineering (General)3
Geography. Anthropology. Recreation: Geography (General)2
Science: Physics1
Technology: Manufactures: Production management. Operations management1
Technology: Electrical engineering. Electronics. Nuclear engineering1
Science: Science (General): Cybernetics: Information theory1
Technology: Electrical engineering. Electronics. Nuclear engineering: Telecommunication1
Science1
Geography. Anthropology. Recreation: Environmental sciences1
The category Technology: Technology (General): Industrial engineering. Management engineering: Applied mathematics. Quantitative methods 9 is the most frequently represented among the references in this article. It primarily includes studies from Journal of Scientific Computing and SIAM Journal on Imaging Sciences. 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
Fuzzy based self-similarity weight estimation in non-local means for gray-scale image de-noising Digital Signal Processing
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics
  • Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks
 2024
Citations Analysis
Category Category Repetition
Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics1
Technology: Electrical engineering. Electronics. Nuclear engineering: Electric apparatus and materials. Electric circuits. Electric networks1
The category Technology: Electrical engineering. Electronics. Nuclear engineering: Electronics 1 is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Fuzzy based self-similarity weight estimation in non-local means for gray-scale image de-noising and was published in 2024. The most recent citation comes from a 2024 study titled Fuzzy based self-similarity weight estimation in non-local means for gray-scale image de-noising. This article reached its peak citation in 2024, with 1 citations. It has been cited in 1 different journals. Among related journals, the Digital Signal Processing cited this research the most, with 1 citations. The chart below illustrates the annual citation trends for this article.
Citations used this article by year