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

Yongsong Li1,2, Zhengzhou Li3,4,5, Kai Wei6,7

  • 1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China. liyongsong@cqu.edu.cn.

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Summary

This study introduces a new homogenous block-based method for accurate noise estimation in images, outperforming existing techniques for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN). The method enhances blind de-noising performance by improving noise level calculation in textured scenes.

Keywords:
Poisson-Gaussian noiseadditive white Gaussian noiseimage sensorlocal gray statistic entropylocal median absolute deviationnoise estimation

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Area of Science:

  • Digital Image Processing
  • Computational Imaging
  • Signal Processing

Background:

  • Accurate noise estimation is crucial for image pre-processing, particularly blind de-noising.
  • Conventional methods for additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN) struggle with heavily textured images, leading to under- or overestimation of noise levels.
  • Developing robust noise estimation techniques for diverse image content is an ongoing challenge.

Purpose of the Study:

  • To propose a novel homogenous block-based noise estimation method for improved accuracy in additive white Gaussian noise (AWGN) and Poisson-Gaussian noise (PGN).
  • To address the limitations of existing methods in estimating noise levels within heavily textured image regions.
  • To enhance the performance of blind de-noising algorithms through more precise noise parameter estimation.

Main Methods:

  • Transformation of noisy images into a local gray statistic entropy (LGSE) map to identify weakly textured blocks.
  • Selection of homogenous blocks with the highest LGSE values in descending order.
  • Computation of local variance in selected blocks using Haar wavelet-based local median absolute deviation (HLMAD).
  • Accurate noise parameter estimation via maximum likelihood estimation (MLE) on local mean and variance.

Main Results:

  • The proposed method demonstrates superior accuracy in estimating noise levels across various scene images and noise intensities compared to state-of-the-art techniques.
  • Experimental results validate the method's effectiveness on synthesized noisy images.
  • The improved noise estimation significantly enhances the performance of blind de-noising algorithms.

Conclusions:

  • The novel homogenous block-based noise estimation method provides a more accurate and robust solution for AWGN and PGN.
  • This approach effectively overcomes the limitations of existing methods in textured image regions.
  • The proposed technique offers a valuable advancement for image pre-processing and blind de-noising applications.