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基于剪切波變換的改進全變分散斑去噪方法

Shearlet-Transform-Based Improved Total Variation Speckle Denoising Method

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摘要

在散斑去噪過程中保持圖像邊緣紋理特征,是光學相干層析圖像處理技術的難題。散斑去噪過程中的散斑殘留和邊緣紋理模糊是該難題的主要誘導因素。為解決這一難題,提出一種基于剪切波變換的改進全變分散斑去噪方法。該方法結合剪切波變換和傳統全變分模型,對不同圖像區域采用針對性的去噪策略,兼顧散斑去噪與紋理保留,提高了光學相干層析圖像的噪聲抑制效果。對不同生理、病理狀態下的視網膜光學相干層析圖像進行測試,結果表明:該方法通過采用區域針對性策略改進了噪聲抑制能力,通過引入剪切波變換方法提高了邊緣紋理保持能力,進而同時實現散斑去除和紋理保留。此外,與其他散斑去噪方法進行對比,驗證了該方法的有效性。

Abstract

In the field of optical coherence tomography, reducing the speckle noise while protecting the textural features of image edge is difficult mainly because of the speckle residue and textural blur of edge in the speckle denoising process. To solve this problem, this study proposes a shearlet-transform-based improved total variation speckle denoising method. By combining the shearlet transform with the traditional total variation model, as well as a targeted denoising strategy applied on different image regions, the proposed method reduces the speckle noise without disturbing the texture in the image, and further improves the speckle-noise suppression in the original optical coherence tomography image. The proposed method is tested on many retinal optical coherence tomography images under different physiological and pathological conditions. Results show that the regional targeted strategy in the proposed method improves the ability of speckle-noise suppression, while the shearlet transform improves the ability of the edge texture protection, resulting in simultaneous speckle reduction and texture protection. The effectiveness of the proposed method is also confirmed in comparison with other common speckle denoising methods.

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補充資料

中圖分類號:T391.4

DOI:10.3788/LOP57.021003

所屬欄目:圖像處理

基金項目:國家自然科學基金;

收稿日期:2019-05-27

修改稿日期:2019-06-26

網絡出版日期:2020-01-01

作者單位    點擊查看

邱岳:天津大學電氣自動化與信息工程學院, 天津 300072
唐晨:天津大學電氣自動化與信息工程學院, 天津 300072
徐敏:天津大學電氣自動化與信息工程學院, 天津 300072
黃圣鑒:天津大學電氣自動化與信息工程學院, 天津 300072
雷振坤:大連理工大學工業裝備結構分析國家重點實驗室, 遼寧 大連116023

聯系人作者:唐晨(tangchen@tju.edu.cn)

備注:國家自然科學基金;

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引用該論文

Qiu Yue,Tang Chen,Xu Min,Huang Shengjian,Lei Zhenkun. Shearlet-Transform-Based Improved Total Variation Speckle Denoising Method[J]. Laser & Optoelectronics Progress, 2020, 57(2): 021003

邱岳,唐晨,徐敏,黃圣鑒,雷振坤. 基于剪切波變換的改進全變分散斑去噪方法[J]. 激光與光電子學進展, 2020, 57(2): 021003

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