 the sparse coded measurements, the redundancy in both components promises recovery from sparse measurements, and the difference between their redundancies promises accurate separation. Mathematically, we cast the reconstruction as a joint optimization, whose solution can be derived by the Augmented Lagrange Method. In our experiment, results on both synthetic data and real data captured by our prototype validate the proposed approach, and we also demonstrate its advantages in two computer vision tasks—photorealistic relighting and segmentation.
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Fig. 1 The system and results of our approach on one exemplary scene. (a) Prototype setup. (b) One coded image. (c, d) Reconstructed reflectance and fluorescence, respectively.
Fig. 2 The visualization of excitation–emission matrix. The strength of the matrix entries is illustrated by intensity here. Vertical and horizontal color bars respectively illustrate the excitation wavelength λin and the emission wavelength, which includes the fluorescent component λ out ref and reflective component λ out fluo. (a) No overlap between excitation and emission. (b) Slight overlap between excitation and emission.
Fig. 3 Extending the reconstruction of a single pixel to image lattice by normalization. Here we use different line colors to differentiate excitation wavelengths.
Fig. 4 The light path of the proposed imaging system. The corresponding real setup is shown in Fig. 1(a).
Fig. 5 The performance on synthetic data, including three materials (horizontal) and three sampling rates (vertical). These results are averaged over five different random codes.
Fig. 6 Performance of our algorithm on a real scene and comparison with that of traverse capturing. The left column contains only reflectance, the middle column is the fluorescence excited by a single band illumination, and the right column gives the result under a mixture-spectrum illumination. The mean absolute percentage error (MAPE) of each result is labeled in the top right corner.
Fig. 7 Quantitative evaluation on real data. Here three representative points are selected, and we differentiate the excitation illuminations with different colors.
Fig. 8 Relighting results under three different types of light sources and comparison with true results. (a) Noon sunlight. (b) Tungsten lamp. (c) Mercury vapor lamp.
Fig. 9 Segmentation assisted by high-spectral-resolution fluorescent components. (a) A scene under daylight. (b) RGB values of five labeled regions. (c) Top three discriminative features between regions 1 and 2. (d) Segmentation of car parts. (e) Top three discriminative features among regions 3, 4, and 5. (f) Segmentation of toy ball and fluorescent paint.Hey Y’all, It’s Fall!
<issue_start><issue_comment>Title: Issues regarding keyboard layout
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