灰度
融合
散射
光学
图像融合
迭代重建
噪音(视频)
医学影像学
图像质量
非负矩阵分解
计算机视觉
人工智能
基质(化学分析)
材料科学
高动态范围成像
算法
图像处理
矩阵分解
高光谱成像
因式分解
物理
航程(航空)
编码(内存)
高动态范围
计算机科学
降噪
计算
前向散射
光学成像
光学相干层析成像
图像复原
对比度(视觉)
比例(比率)
像素
图像(数学)
作者
Haiming Yuan,Fei Wang,Jingdan Liu,Guohai Situ
标识
DOI:10.1002/lpor.202501315
摘要
Abstract Optical imaging through inhomogeneous scattering media is essential, particularly in medical imaging, where enhanced penetration depth and an expanded field‐of‐view (FOV) are urgently demanded. Non‐negative matrix factorization (NMF) provides an effective solution for large FOV non‐invasive imaging through scattering layers. However, the emerging NMF requires extensive measurement data across multiple encoding patterns. Furthermore, NMF reconstructions often suffer from loss of grayscale accuracy and the inclusion of background noise. Here, an innovative method is presented that leverages encoding‐sparsity optimization (ESO) to decrease the amount of data required by approximately an order of magnitude. Additionally, a precise reconstruction algorithm is introduced using Localization and Grayscale‐Fusion (LG‐Fusion), which eliminates background noise and extends the FOV to 4.3 times the memory effect range (MER). The technique enables efficient, high‐quality imaging with large FOVs through a 200‐‐thick mouse brain.
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