scholarly journals Iterative Estimation Algorithms Using Conjugate Function Lower Bound and Minorization-Maximization with Applications in Image Denoising

2008 ◽  
Vol 2008 (1) ◽  
Author(s):  
Guang Deng ◽  
Wai-Yin Ng
Keyword(s):  
Lower Bound ◽  
Image Denoising ◽  
Conjugate Function ◽  
Iterative Estimation ◽  
Estimation Algorithms

scholarly journals Variance lower bound on fluorescence microscopy image denoising

2020 ◽  
Vol 11 (12) ◽  
pp. 6973
Author(s):  
Yilun Li ◽  
Sheng Liu ◽  
Fang Huang
Keyword(s):  
Lower Bound ◽  
Fluorescence Microscopy ◽  
Image Denoising ◽  
Microscopy Image

scholarly journals Least Squares Based and Two-Stage Least Squares Based Iterative Estimation Algorithms for H-FIR-MA Systems

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Zhenwei Shi ◽  
Zhicheng Ji
Keyword(s):  
Least Squares ◽  
Iterative Algorithm ◽  
Impulse Response ◽  
Finite Impulse Response ◽  
Moving Average ◽  
Two Stage ◽  
Iterative Estimation ◽  
Estimation Algorithms ◽  
Two Stage Least Squares

This paper studies the identification of Hammerstein finite impulse response moving average (H-FIR-MA for short) systems. A new two-stage least squares iterative algorithm is developed to identify the parameters of the H-FIR-MA systems. The simulation cases indicate the efficiency of the proposed algorithms.

scholarly journals Iterative estimation of total π-electron energy

2005 ◽  
Vol 70 (10) ◽  
pp. 1193-1197 ◽  
Author(s):  
Lemi Türker ◽  
Ivan Gutman
Keyword(s):  
Lower Bound ◽  
Electron Energy ◽  
Upper Bound ◽  
Upper Bounds ◽  
Lower And Upper Bounds ◽  
Benzenoid Hydrocarbons ◽  
Total Electron ◽  
Iterative Estimation ◽  
Total Electron Energy ◽  
Almost All

In this work, the lower and upper bounds for total ?-electron energy (E) was studied. A method is presented, by means of which, starting with a lower bound EL and an upper bound EU for E, a sequence of auxiliary quantities E0 E1, E2,? is computed, such that E0 = EL, E0 < E1 < E2 < ?, and E = EU. Therefore, an integer k exists, such that Ek E < Ek+1. If the estimates EL and EU are of the McClelland type, then k is called the McClelland number. For almost all benzenoid hydrocarbons, k = 3.

scholarly journals Variance lower bound on fluorescence microscopy image denoising

2020 ◽  
Author(s):  
Yilun Li ◽  
Sheng Liu ◽  
Fang Huang
Keyword(s):  
Lower Bound ◽  
Fluorescence Microscopy ◽  
Image Denoising ◽  
High Speed ◽  
Signal To Noise Ratio ◽  
Photon Counting ◽  
Numerical Aperture ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Microscopy Image

The signal to noise ratio of high-speed fluorescence microscopy is heavily influenced by photon counting noise and sensor noise due to the expected low photon budget. Denoising algorithms are developed to decrease these noise fluctuations in the microscopy data. One question arises: whether there exists a theoretical precision limit for the performance of a denoising algorithm. In this paper, combining Cramér-Rao Lower Bound with constraints and the low-pass-filter property of microscope systems, we develop a method providing a theoretical variance lower bound of microscopy image denoising. We show that this lower bound is influenced by photon count, readout noise, detection wavelength, effective pixel size and the numerical aperture of the microscope system. We demonstrate our development by comparing multiple state-of-the-art denoising algorithms to this bound. This theoretical bound provides a reference benchmark for microscopy denoising algorithms, and establishes a framework to incorporate additional prior knowledge into theoretical denoising performance limit calculation.

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