Research on the deep learning-based exposure invariant spectral reconstruction method - Liang, Jinxing; Xin, Lei; Zuo, Zhuan; Zhou, Jing; Liu, Anping; Luo, Hang; Hu, Xinrong (Frontiers in Neuroscience, 2022)
The surface spectral reflectance of object is the key factor for high-fidelity color reproduction and material analysis, and spectral acquisition is the basis of its applications. Based on the theoretical imaging model of digital camera, the spectral reflectance of any pixels in the image can be obtained through spectral reconstruction technology. This technology can avoid the application limitations of spectral cameras in open scenarios and obtain high spatial resolution multispectral images. However, the current spectral reconstruction algorithms are sensitive to the exposure variant of the test images. That is, when the exposure of the test image is different from that of the training image, the reconstructed spectral curve of the test object will deviate from the real spectral to varying degrees, which will lead to the spectral data of the target object cannot be accurately reconstructed. In this paper, using the current deep learning-based spectral reconstruction framework, an optimized method for spectral reconstruction based on data augmentation and attention mechanism is proposed. The proposed method is exposure invariant and will adapt to the open environment of which the light is easy changed and the illumination is non-uniformity. Thus, the robustness and reconstruction accuracy of the spectral reconstruction model in practical applications are improved. The experiments show that the proposed method can accurately reconstruct the shape of the spectral reflectance curve of the test object under different test exposure levels. And the spectral reconstruction error of our method at different exposure levels is significantly lower than that of the existing methods, which verifies the effectiveness of the proposed method and its superiority.