Abstract
Deep convolutional neural networks (CNNs) have been successfully applied to spectral reconstruction (SR) and acquired superior performance. Nevertheless, the existing CNN-based SR approaches integrate hierarchical features from different layers indiscriminately, lacking an investigation of the relationships of intermediate feature maps, which limits the learning power of CNNs. To tackle this problem, we propose a deep residual augmented attentional u-shape network (RA2UN) with several double improved residual blocks (DIRB) instead of paired plain convolutional units. Specifically, a trainable spatial augmented attention (SAA) module is developed to bridge the encoder and decoder to emphasize the features in the informative regions. Furthermore, we present a novel channel augmented attention (CAA) module embedded in the DIRB to rescale adaptively and enhance residual learning by using first-order and second-order statistics for stronger feature representations. Finally, a boundary-aware constraint is employed to focus on the salient edge information and recover more accurate high-frequency details. Experimental results on four benchmark datasets demonstrate that the proposed RA2UN network outperforms the state-of-the-art SR methods under quantitative measurements and perceptual comparison.
Highlights
Hyperspectral imaging systems can record the actual scene spectra over a large set of narrow spectral bands [1]
To model interdependencies among channels of intermediate feature maps, we present a novel channel augmented attention (CAA) module embedded in the double improved residual blocks (DIRB) to adaptively recalibrate channel-wise feature responses and enhance residual learning by using first-order and second-order statistics for stronger feature expression
In order to demonstrate the effectiveness of the spatial augmented attention (SAA) module, the CAA module and the boundary-aware constraint, we conduct the ablation study on the NTIRE2020 “Clean”
Summary
Hyperspectral imaging systems can record the actual scene spectra over a large set of narrow spectral bands [1]. In contrast to the ordinary cameras record only reflectance or transmittance of three spectral bands (i.e., Red, Green, and Blue), hyperspectral spectrometers can encode hyperspectral images (HSIs) by obtaining continuous spectrums on each pixel of the object. The abundant spectral signatures are beneficial to many computer vision tasks, such as face recognition [2], image classification [3,4] and object tracking [5], etc. Traditional scanning HSIs acquisition systems rely on either 1D line or 2D plane scanning (e.g., whiskbroom [6], pushbroom [7] or variable-filter technology [8]) to encode spectral information of the scene. Whiskbroom imaging devices apply mirrors and fiber optics to collect reflected hyperspectral signals point by point.
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