Poisson-Gaussian Noise Reduction for X-Ray Images Based on Local Linear Minimum Mean Square Error Shrinkage in Nonsubsampled Contourlet Transform Domain

Sangyoon Lee,Moon Gi Kang

doi:10.1109/access.2021.3097078

Abstract

Noise reduction is important for X-ray images because it can reduce radiation exposure to patients. X-ray image noise has a Poisson-Gaussian distribution, and recently, noise analysis and removal in multiscale transformations have been widely implemented. The nonsubsampled contourlet transform (NSCT) is a multiscale transformation suitable for medical images that separates the scale and direction. This study proposes a Poisson-Gaussian noise-removal method using NSCT shrinkage that is based on the characteristics of Poisson-Gaussian noise in NSCT domain. It has the structure of a block-matching 3D filtering algorithm in the form of basic estimation and noise removal process; however, the main processes are modified to consider Poisson-Gaussian noise characteristics. In the basic estimation process, an NSCT shrinkage method that is suitable for Poisson-Gaussian noise characteristics is developed by optimizing the local linear minimum mean square error estimator in the NSCT domain. In the denoising step, the noise term of the Wiener filter is determined using the result of the NSCT shrinkage, and finally, the denoised image is obtained. The proposed method is applied to simulated and real X-ray images and is compared with other state-of-the-art Poisson-Gaussian noise removal methods; it exhibits excellent results in both quantitative and qualitative aspects.

Highlights

Image acquisition devices acquire digital images by converting light into electrical signals via complementary metal oxide semiconductors or charged coupled devices image sensors [1], [2]
It is difficult to obtain a ground truth image for an X-ray image; experiments were conducted by artificially adding noise to noiseless images to quantitatively evaluate the noise removal effect
We proposed the local linear minimum mean square error (LLMMSE) shrinkage method in the nonsubsampled contourlet transform (NSCT) based on noise characteristics using an experimental analysis and a theoretical approach

Summary

Introduction

Image acquisition devices acquire digital images by converting light into electrical signals via complementary metal oxide semiconductors or charged coupled devices image sensors [1], [2]. The intensity of the image is determined by the number of photons incident on the sensor, and the number of photons follows a Poisson distribution. This is the primary cause of noise in the image, which has Poisson characteristics. The noise of an image acquired in a general situation (medium, high-illumination situation) primarily follows a Gaussian distribution because the number of photons incident on the sensor is sufficiently large; The associate editor coordinating the review of this manuscript and approving it for publication was Shiqi Wang. In low-light, ultra-low-light conditions, or when the number of photons incident on the sensor is small, the image noise cannot be approximated by a Gaussian distribution, and it follows the Poisson distribution [3], [4]. Noise generated by sensors or other electronic devices follows a Gaussian distribution, and it is modeled using a combination of Poisson and Gaussian distribution, called a Poisson-Gaussian distribution [5]

Methods

Results

Conclusion