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Hyperspectral Image Recovery via Hybrid Regularization.

Reza Arablouei, Frank de Hoog

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|January 24, 2017

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This study introduces a new method to recover hyperspectral images from limited, noisy data by leveraging spatial and spectral correlations. The algorithm demonstrates superior performance compared to existing methods, even with significantly reduced measurements.

Area of Science:

Image Processing
Computational Imaging
Remote Sensing

Background:

Hyperspectral images contain rich spectral information but are often large and difficult to acquire completely.
Natural images possess inherent spatial smoothness and spectral correlations that can be exploited for data recovery.
Existing methods for hyperspectral image recovery may not fully utilize these intrinsic image properties.

Purpose of the Study:

To develop an efficient algorithm for recovering hyperspectral images from incomplete and noisy measurements.
To leverage spatial and spectral prior knowledge of natural images for improved reconstruction accuracy.
To analyze the convergence and performance of the proposed recovery algorithm.

Main Methods:

Formulation of a composite cost function with data-fitting and regularization terms.

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Spatial regularization using the sum of total variation across spectral bands.

Spectral regularization employing the ℓ1-norm of coefficients from a sparsifying transform.

Minimization of the cost function using an accelerated proximal-subgradient method.

Main Results:

The proposed algorithm achieves excellent hyperspectral image recovery performance using a fraction of the full data size.
Convergence of the accelerated proximal-subgradient method is proven.
The algorithm significantly outperforms the classical basis-pursuit denoising approach for hyperspectral image recovery.

Conclusions:

The developed method effectively exploits spatial and spectral priors for accurate hyperspectral image reconstruction.
The accelerated proximal-subgradient algorithm provides a robust and efficient solution for hyperspectral imaging challenges.
This approach offers a promising direction for acquiring high-quality hyperspectral data with reduced measurement requirements.