A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model
By Jianing Shi, Stanley J. Osher. SIAM Journal of Imaging Sciences, 2008
309 citations recorded by the source corpus.
Subfields: Computational Mechanics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/eeadeb5bad286c28334b312527b5512018658f30
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mechanics
- Paolo Rocca
- Giacomo Oliveri
- Andrea Massa
- Luciano Alparone
- Tiziano Bianchi
- Fabrizio Argenti
- Alessandro Lapini
- Ramalingam Chellappa
In Computer Vision and Pattern Recognition
- Vishal M. Patel
- Ramalingam Chellappa
- Tiziano Bianchi
- Alessandro Lapini
- Fabrizio Argenti
- Luciano Alparone
- Paolo Rocca
- Andrea Massa
Citations in the corpus
- Compressed Synthetic Aperture Radar (2010, methodology): “Inspired by the properties of the jittered sampling, we propose to apply jittered undersampling in slow-time as well.”
- A Tutorial on Speckle Reduction in Synthetic Aperture Radar Images (2013, methodology): “(47) Another natural solution is to consider a quadratic data fidelity term [117], [120], which yields”
- Removing Multiplicative Noise by Douglas-Rachford Splitting Methods (2010, methodology): “The signal and noise level were chosen in accordance with the experiments in [59].”
- Compressive Sensing in Electromagnetics - A Review (2015, methodology): “antenna beam to illuminate the terrain patch being imaged) [37, 86, 87] where both TV (19) [37] and BCS (20) [86] formulations have been assessed in undersampling SAR data, increasing the robustness to noise, and reducing the sidelobes in retrieved images despite the use of simple, but efficient, re”
- A New Total Variation Method for Multiplicative Noise Removal (2009, unknown): “Recently, Shi and Osher [19] proposed considering a noisy observation log g = log u + log v and derived the total variation (TV) minimization model for multiplicative noise removal problems.”
- Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization (2009, unknown): “The combination of TV regularization with the log-likelihood resulting from the multiplicative observation model leads to an objective function with a nonquadratic term (the log-likelihood) plus a nonsmooth term (the TV regularizer), to which some research work has been recently devoted [1], [27], [”