By simultaneously introducing a finite-difference-based numerical loss term and a clustering-reconstruction mechanism,we propose an enhanced physics-informed neural network named the informed reconstruction-oriented n...By simultaneously introducing a finite-difference-based numerical loss term and a clustering-reconstruction mechanism,we propose an enhanced physics-informed neural network named the informed reconstruction-oriented numerical network(IRON-Net)and subsequently apply it to the Manakov equations-a well-known two-component nonlinear physical model.Numerical experiments are conducted on a dataset containing eight analytical solu-tions with noise.The results indicate that,compared to conventional PINNs and other mainstream algorithms,IRON-Net demonstrates significant advantages in training accuracy,convergence rate,and robustness,achiev-ing a stepwise improvement in the neural network’s ability to enforce physical constraints.Additional ablation experiments further confirm the necessity of the consistency constraint within IRON-Net.This study provides an effective approach for modeling and parameter identification in complex nonlinear optical systems as well as other nonlinear physical scenarios.展开更多
Coded aperture snapshot spectral imaging(CASSI) has been discussed in recent years. It has the remarkable advantages of high optical throughput, snapshot imaging, etc. The entire spatial-spectral data-cube can be reco...Coded aperture snapshot spectral imaging(CASSI) has been discussed in recent years. It has the remarkable advantages of high optical throughput, snapshot imaging, etc. The entire spatial-spectral data-cube can be reconstructed with just a single two-dimensional(2D) compressive sensing measurement. On the other hand, for less spectrally sparse scenes,the insufficiency of sparse sampling and aliasing in spatial-spectral images reduce the accuracy of reconstructed threedimensional(3D) spectral cube. To solve this problem, this paper extends the improved CASSI. A band-pass filter array is mounted on the coded mask, and then the first image plane is divided into some continuous spectral sub-band areas. The entire 3D spectral cube could be captured by the relative movement between the object and the instrument. The principle analysis and imaging simulation are presented. Compared with peak signal-to-noise ratio(PSNR) and the information entropy of the reconstructed images at different numbers of spectral sub-band areas, the reconstructed 3D spectral cube reveals an observable improvement in the reconstruction fidelity, with an increase in the number of the sub-bands and a simultaneous decrease in the number of spectral channels of each sub-band.展开更多
基金supported by the Hubei Provincial Natural Science Foundation(Grant No.2023AFB873)the National Natural Science Foun-dation of China(Grant Nos.12505006,11975172,122611-31495,and 12381240286).
文摘By simultaneously introducing a finite-difference-based numerical loss term and a clustering-reconstruction mechanism,we propose an enhanced physics-informed neural network named the informed reconstruction-oriented numerical network(IRON-Net)and subsequently apply it to the Manakov equations-a well-known two-component nonlinear physical model.Numerical experiments are conducted on a dataset containing eight analytical solu-tions with noise.The results indicate that,compared to conventional PINNs and other mainstream algorithms,IRON-Net demonstrates significant advantages in training accuracy,convergence rate,and robustness,achiev-ing a stepwise improvement in the neural network’s ability to enforce physical constraints.Additional ablation experiments further confirm the necessity of the consistency constraint within IRON-Net.This study provides an effective approach for modeling and parameter identification in complex nonlinear optical systems as well as other nonlinear physical scenarios.
基金supported by the National Natural Science Foundation for Distinguished Young Scholars of China(Grant No.61225024)the National High Technology Research and Development Program of China(Grant No.2011AA7012022)
文摘Coded aperture snapshot spectral imaging(CASSI) has been discussed in recent years. It has the remarkable advantages of high optical throughput, snapshot imaging, etc. The entire spatial-spectral data-cube can be reconstructed with just a single two-dimensional(2D) compressive sensing measurement. On the other hand, for less spectrally sparse scenes,the insufficiency of sparse sampling and aliasing in spatial-spectral images reduce the accuracy of reconstructed threedimensional(3D) spectral cube. To solve this problem, this paper extends the improved CASSI. A band-pass filter array is mounted on the coded mask, and then the first image plane is divided into some continuous spectral sub-band areas. The entire 3D spectral cube could be captured by the relative movement between the object and the instrument. The principle analysis and imaging simulation are presented. Compared with peak signal-to-noise ratio(PSNR) and the information entropy of the reconstructed images at different numbers of spectral sub-band areas, the reconstructed 3D spectral cube reveals an observable improvement in the reconstruction fidelity, with an increase in the number of the sub-bands and a simultaneous decrease in the number of spectral channels of each sub-band.
