Speckle Noise Removal by Energy Models with New Regularization Setting

Bo Chen,Weiqiang Zhang,Jinbin Zou

doi:10.1155/2020/3936975

Bo Chen, Weiqiang Zhang + Show 1 more

Open Access

https://doi.org/10.1155/2020/3936975

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Abstract

In this paper, we introduce two novel total variation models to deal with speckle noise in ultrasound image in order to retain the fine details more effectively and to improve the speed of energy diffusion during the process. Firstly, two new convex functions are introduced as regularization term in the adaptive total variation model, and then, the diffusion performances of Hypersurface Total Variation (HYPTV) model and Logarithmic Total Variation (LOGTV) model are analyzed mathematically through the physical characteristics of local coordinates. We have shown that the larger positive parameter in the model is set, the greater energy diffusion speed appears to be, but it will cause the image to be too smooth that required adequate attention. Numerical experimental results show that our proposed LOGTV model for speckle noise removal is superior to traditional models, not only in visual effect but also in quantitative measures.

Highlights

With the development of digital image technology, a large number of digital images are transmitted and compressed through various channels
The new proposed Hypersurface Total Variation (HYPTV) model and the Logarithmic Total Variation (LOGTV) model, as shown in this paper, can preserve the edges of the restored images well when restoring the ultrasonic image with speckle noise and better reduce the staircase effect generated during the recovery process
We calculated the noise deviation reduction (NDR) at each iteration as a convergence condition; we present numerical results to demonstrate the effectiveness of the HYPTV and LOGTV model

Summary

Introduction

With the development of digital image technology, a large number of digital images are transmitted and compressed through various channels. We focus on the image denoising form by using variation method, where the noisy image is ultrasound speckle noise. Fractional theory [29, 30], wavelet [31], and statistical information [32,33,34] are employed to deal with intensity inhomogeneity or noise These denoising methods can reduce the staircase effects in the restoration of additive noise images, there are more staircase effects that appeared in the restoration of speckle noise images. The new proposed HYPTV model and the LOGTV model, as shown in this paper, can preserve the edges of the restored images well when restoring the ultrasonic image with speckle noise and better reduce the staircase effect generated during the recovery process.

Preliminary

The Proposed Restoration Model

Concluding Remarks