Snapshot Multiplexed Imaging Based on Compressive Sensing

Fu, Ying; Sun, Chen; Wang, Lizhi; Huang, Hua

doi:10.1007/978-3-030-00767-6_43

Cited by 2 publications

(1 citation statement)

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“…Instead of relying on the development of hardware, many computational methods, such as compressive sensing-based [5], [6], [7], [8], [9], [10], [11] , HS and RGB image fusion [12], [13], [14], [15], [16], [17], single RGB imagebased [18], [19], [20], [21], and spatial super-resolution [22], [23], [24], [25], [26], have been proposed to acquire HS images in an affordable and convenient manner. Particularly, reconstructing HS images from single RGB images, which does not require specially-designed acquisition hardware, is a promising direction.…”

Section: Introductionmentioning

confidence: 99%

Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images

Zhu

Liu

Hou

et al. 2021

Preprint

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This paper investigates the problem of recovering hyperspectral (HS) images from single RGB images. To tackle such a severely ill-posed problem, we propose a physicallyinterpretable, compact, efficient, and end-to-end learning-based framework, namely AGD-Net. Precisely, by taking advantage of the imaging process, we first formulate the problem explicitly based on the classic gradient descent algorithm. Then, we design a lightweight neural network with a multi-stage architecture to mimic the formed amended gradient descent process, in which efficient convolution and novel spectral zero-mean normalization are proposed to effectively extract spatial-spectral features for regressing an initialization, a basic gradient, and an incremental gradient. Besides, based on the approximate low-rank property of HS images, we propose a novel rank loss to promote the similarity between the global structures of reconstructed and ground-truth HS images, which is optimized with our singular value weighting strategy during training. Moreover, AGD-Net, a single network after one-time training, is flexible to handle the reconstruction with various spectral response functions. Extensive experiments over three commonly-used benchmark datasets demonstrate that AGD-Net can improve the reconstruction quality by more than 1.0 dB on average while saving 67× parameters and 32× FLOPs, compared with state-of-the-art methods. The code will be publicly available at https://github.com/zbzhzhy/GD-Net.

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