Medical image denoising using simple form of MMSE estimation in Poisson–Gaussian noise model

2016 ◽  
Vol 09 (02) ◽  
pp. 1650020 ◽  
Author(s):  
Pichid Kittisuwan
Keyword(s):  
Simple Form ◽  
Gaussian Noise ◽  
Medical Image ◽  
Additive White Gaussian Noise ◽  
Minimum Mean Square Error ◽  
Estimation Method ◽  
Imaging Systems ◽  
Noise Model ◽  
Logistic Distribution ◽  
Noise Reduction Algorithm

Poisson–Gaussian noise is the basis of image formation for a great number of imaging systems used in variety of applications, including medical and astronomical imaging. In wavelet domain, the application of Bayesian estimation method with generalized Anscombe transform in Poisson–Gaussian noise reduction algorithm has shown remarkable success over the last decade. The generalized Anscombe transform is exerted to convert the Poisson–Gaussian noise into an additive white Gaussian noise (AWGN). So, the resulting data can be denoised with any algorithm designed for the removal of AWGN. Here, we present simple form of minimum mean square error (MMSE) estimator for logistic distribution in Poisson–Gaussian noise. The experimental results show that the proposed method yields good denoising results.

scholarly journals Noise Estimation for Image Sensor Based on Local Entropy and Median Absolute Deviation

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 339 ◽  
Author(s):  
Yongsong Li ◽  
Zhengzhou Li ◽  
Kai Wei ◽  
Weiqi Xiong ◽  
Jiangpeng Yu ◽  
...  
Keyword(s):  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Estimation Method ◽  
Likelihood Estimation ◽  
Haar Wavelet ◽  
Image Sensor ◽  
Median Absolute Deviation ◽  
Noise Estimation ◽  
Estimation Methods ◽  
Absolute Deviation

Noise estimation for image sensor is a key technique in many image pre-processing applications such as blind de-noising. The existing noise estimation methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) may underestimate or overestimate the noise level in the situation of a heavy textured scene image. To cope with this problem, a novel homogenous block-based noise estimation method is proposed to calculate these noises in this paper. Initially, the noisy image is transformed into the map of local gray statistic entropy (LGSE), and the weakly textured image blocks can be selected with several biggest LGSE values in a descending order. Then, the Haar wavelet-based local median absolute deviation (HLMAD) is presented to compute the local variance of these selected homogenous blocks. After that, the noise parameters can be estimated accurately by applying the maximum likelihood estimation (MLE) to analyze the local mean and variance of selected blocks. Extensive experiments on synthesized noised images are induced and the experimental results show that the proposed method could not only more accurately estimate the noise of various scene images with different noise levels than the compared state-of-the-art methods, but also promote the performance of the blind de-noising algorithm.

Differential Form of Bivariate MMSE Estimator Based on Gaussian Noise

2016 ◽  
Vol 26 (01) ◽  
pp. 1750008
Author(s):  
P. Kittisuwan ◽  
C. Chinrungrueng
Keyword(s):  
Differential Form ◽  
Gaussian Noise ◽  
Additive White Gaussian Noise ◽  
Minimum Mean Square Error ◽  
Integral Form ◽  
Noise Signal ◽  
Original Form ◽  
Reduction Methods ◽  
Mmse Estimator ◽  
Heavy Tailed

In fact, the noise signal is an important problem in signal, circuits and systems. The minimum mean square error (MMSE) estimation technique is useful in several additive white Gaussian noise (AWGN) reduction methods. Original form of MMSE estimator is the integral form. Unfortunately, integral form of MMSE estimator cannot be obtained in simple form for any interesting peaked, heavy-tailed densities (also known as super-Gaussian densities). In this work, we proposed a differential form of bivariate MMSE estimator. The development depends on bivariate Taylor series. The proposed estimator requires no integration. In fact, the derivation is an extension of the existing results for differential form of univariate MMSE estimator.

scholarly journals Adaptation Strategies for Applying AWGN-Based Denoiser to Realistic Noise

2019 ◽  
Vol 33 ◽  
pp. 10085-10086
Author(s):  
Yuqian Zhou ◽  
Jianbo Jiao ◽  
Haibin Huang ◽  
Jue Wang ◽  
Thomas Huang
Keyword(s):  
Gaussian Noise ◽  
Impulse Noise ◽  
Additive White Gaussian Noise ◽  
Super Resolution ◽  
Adaptation Strategies ◽  
Noise Model ◽  
Discriminative Learning ◽  
Preliminary Results ◽  
Multi Scale ◽  
Multiple Strategies

Discriminative learning based denoising model trained with Additive White Gaussian Noise (AWGN) performs well on synthesized noise. However, realistic noise can be spatialvariant, signal-dependent and a mixture of complicated noises. In this paper, we explore multiple strategies for applying an AWGN-based denoiser to realistic noise. Specifically, we trained a deep network integrating noise estimating and denoiser with mixed Gaussian (AWGN) and Random Value Impulse Noise (RVIN). To adapt the model to realistic noises, we investigated multi-channel, multi-scale and super-resolution approaches. Our preliminary results demonstrated the effectiveness of the newly-proposed noise model and adaptation strategies.

scholarly journals Threshold Computation for Spatially Coupled Turbo-Like Codes on the AWGN Channel

Entropy ◽  
2021 ◽  
Vol 23 (2) ◽  
pp. 240
Author(s):  
Muhammad Umar Farooq ◽  
Alexandre Graell i Amat ◽  
Michael Lentmaier
Keyword(s):  
Gaussian Noise ◽  
Belief Propagation ◽  
Additive White Gaussian Noise ◽  
Maximum A Posteriori ◽  
Prediction Methods ◽  
Threshold Analysis ◽  
A Posteriori ◽  
Awgn Channel ◽  
Efficient Prediction ◽  
Binary Erasure Channel

In this paper, we perform a belief propagation (BP) decoding threshold analysis of spatially coupled (SC) turbo-like codes (TCs) (SC-TCs) on the additive white Gaussian noise (AWGN) channel. We review Monte-Carlo density evolution (MC-DE) and efficient prediction methods, which determine the BP thresholds of SC-TCs over the AWGN channel. We demonstrate that instead of performing time-consuming MC-DE computations, the BP threshold of SC-TCs over the AWGN channel can be predicted very efficiently from their binary erasure channel (BEC) thresholds. From threshold results, we conjecture that the similarity of MC-DE and predicted thresholds is related to the threshold saturation capability as well as capacity-approaching maximum a posteriori (MAP) performance of an SC-TC ensemble.

scholarly journals Revisiting the nonlinear Gaussian noise model for hybrid fiber spans

2021 ◽  
Vol 2 (1) ◽  
pp. 30-49
Author(s):  
Ioannis Roudas ◽  
Jaroslaw Kwapisz ◽  
Xin Jiang
Keyword(s):  
Gaussian Noise ◽  
Noise Model ◽  
Hybrid Fiber
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