Direct reconstruction of qualitative depth information from turbid media by a single hyper spectral image

2019 ◽  
Author(s):  
Benjamin Lengenfelder ◽  
Martin Hohmann ◽  
Damaris Hecht ◽  
Florian Klämpfl ◽  
Michael Schmidt
Keyword(s):  
Turbid Media ◽  
Depth Information ◽  
Spectral Image ◽  
Hyper Spectral

scholarly journals Proof of Principle for Direct Reconstruction of Qualitative Depth Information from Turbid Media by a Single Hyper Spectral Image

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2860
Author(s):  
Martin Hohmann ◽  
Damaris Hecht ◽  
Benjamin Lengenfelder ◽  
Moritz Späth ◽  
Florian Klämpfl ◽  
...  
Keyword(s):  
Opposite Effect ◽  
Three Dimensional ◽  
Turbid Media ◽  
Dimensional Structure ◽  
Depth Information ◽  
Medical Applications ◽  
Spectral Image ◽  
Hyper Spectral ◽  
Hyper Spectral Imaging ◽  
Proof Of Principle

In medical applications, hyper-spectral imaging is becoming more and more common. It has been shown to be more effective for classification and segmentation than normal RGB imaging because narrower wavelength bands are used, providing a higher contrast. However, until now, the fact that hyper-spectral images also contain information about the three-dimensional structure of turbid media has been neglected. In this study, it is shown that it is possible to derive information about the depth of inclusions in turbid phantoms from a single hyper-spectral image. Here, the depth information is encoded by a combination of scattering and absorption within the phantom. Although scatter-dominated regions increase the backscattering for deep vessels, absorption has the opposite effect. With this argumentation, it makes sense to assume that, under certain conditions, a wavelength is not influenced by the depth of the inclusion and acts as an iso-point. This iso-point could be used to easily derive information about the depth of an inclusion. In this study, it is shown that the iso-point exists in some cases. Moreover, it is shown that the iso-point can be used to obtain precise depth information.

Band selection by mutual information for hyper-spectral image classification

2016 ◽  
Vol 8 (1) ◽  
pp. 98 ◽  
Author(s):  
Ibtissam Banit' ◽  
N.A. ouagua ◽  
Mounir Ait Kerroum ◽  
Ahmed Hammouch ◽  
Driss Aboutajdine
Keyword(s):  
Mutual Information ◽  
Image Classification ◽  
Band Selection ◽  
Spectral Image ◽  
Hyper Spectral

Hyper-spectral image classification using spatial-spectral manifold reconstruction

2018 ◽  
Vol 26 (7) ◽  
pp. 1827-1836
Author(s):  
黄 鸿 HUANG Hong ◽  
陈美利 CHEN Mei-li ◽  
段宇乐 DUAN Yu-le ◽  
石光耀 SHI Guang-yao
Keyword(s):  
Image Classification ◽  
Spectral Image ◽  
Hyper Spectral ◽  
Manifold Reconstruction

scholarly journals The Hyper-spectral Image Compression Based on K-Means Clustering and Parallel Prediction Algorithm*

2018 ◽  
Vol 173 ◽  
pp. 03071
Author(s):  
Wu Wenbin ◽  
Yue Wu ◽  
Jintao Li
Keyword(s):  
Image Compression ◽  
Clustering Algorithm ◽  
Compression Algorithm ◽  
Prediction Algorithm ◽  
Two Dimensional ◽  
Spectral Image ◽  
Compression Algorithms ◽  
Serial Processing ◽  
Compression Time ◽  
Hyper Spectral

In this paper, we propose a lossless compression algorithm for hyper-spectral images with the help of the K-Means clustering and parallel prediction. We use K-Means clustering algorithm to classify hyper-spectral images, and we obtain a number of two dimensional sub images. We use the adaptive prediction compression algorithm based on the absolute ratio to compress the two dimensional sub images. The traditional prediction algorithm is adopted in the serial processing mode, and the processing time is long. So we improve the efficiency of the parallel prediction compression algorithm, to meet the needs of the rapid compression. In this paper, a variety of hyper-spectral image compression algorithms are compared with the proposed method. The experimental results show that the proposed algorithm can effectively improve the compression ratio of hyper-spectral images and reduce the compression time effectively.

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