HyperMixNet: Hyperspectral Image Reconstruction with Deep Mixed Network from a Snapshot Measurement

Hyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.

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Parallel hyperspectral image reconstruction using random projections

10.1117/12.2241252 ◽

2016 ◽

Author(s):

Jorge Sevilla ◽

Gabriel Martín ◽

José M. P. Nascimento

Keyword(s):

Image Reconstruction ◽

Hyperspectral Image ◽

Random Projections

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Coded Aperture Hyperspectral Image Reconstruction

Sensors ◽

10.3390/s21196551 ◽

2021 ◽

Vol 21 (19) ◽

pp. 6551

Author(s):

Ignacio García-Sánchez ◽

Óscar Fresnedo ◽

José P. González-Coma ◽

Luis Castedo

Keyword(s):

Image Reconstruction ◽

Hyperspectral Image ◽

Computational Cost ◽

Hyperspectral Images ◽

Coded Aperture ◽

Block Model ◽

Reconstruction Procedure ◽

Estimation Algorithms ◽

Efficient Mechanisms

In this work, we study and analyze the reconstruction of hyperspectral images that are sampled with a CASSI device. The sensing procedure was modeled with the help of the CS theory, which enabled efficient mechanisms for the reconstruction of the hyperspectral images from their compressive measurements. In particular, we considered and compared four different type of estimation algorithms: OMP, GPSR, LASSO, and IST. Furthermore, the large dimensions of hyperspectral images required the implementation of a practical block CASSI model to reconstruct the images with an acceptable delay and affordable computational cost. In order to consider the particularities of the block model and the dispersive effects in the CASSI-like sensing procedure, the problem was reformulated, as well as the construction of the variables involved. For this practical CASSI setup, we evaluated the performance of the overall system by considering the aforementioned algorithms and the different factors that impacted the reconstruction procedure. Finally, the obtained results were analyzed and discussed from a practical perspective.

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