Speckle noise reduction for digital holographic images using Swin Transformer

被引：0

作者：

Xie, Zhaoqian ^{[1
]}

Chen, Li ^{[1
,2
]}

Chen, Honghui ^{[1
]}

Wen, Kunhua ^{[1
]}

Guo, Junwei ^{[1
]}

机构：

[1] Guangdong Univ Technol, Sch Phys & Optoelect Engn, Guangzhou 510006, Peoples R China

[2] Guangdong Univ Technol, Guangdong Prov Key Lab Informat Photon Technol, Guangzhou 510006, Peoples R China

来源：

OPTICS AND LASERS IN ENGINEERING | 2025年 / 184卷

基金：

中国国家自然科学基金;

关键词：

Speckle Noise; Digital Holography; Deep Learning; Transformer; IMPROVEMENT; TOMOGRAPHY;

D O I：

10.1016/j.optlaseng.2024.108605

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

We introduce an innovative approach for reducing speckle noise in holographic reconstruction images utilizing the Transformer architecture. This approach not only effectively captures speckle noise from digital holographic images but also better preserves details in images, owing to the characteristics of the Swin Transformer in globally and locally capturing relationships between image features. The network is trained using a large dataset with a distribution similar to real speckle noise. Experimental results demonstrate outstanding denoising performance of the proposed method and effectively preserving the details.

引用

页数：7

共 32 条

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