Restoration Method of Hadamard Coding Spectral Imager

Hadamard coding spectral imaging technology is a computational spectral imaging technology, which modulates the target’s spectral information and recovers the original spectrum by inverse transformation. Because it has the advantage of multichannel detection, it is being studied by more researchers. For the engineering realization of push-broom coding spectral imaging instrument, it will inevitably be subjected to push-broom error, template error and detection noise, the redundant sampling problem caused by detector. Therefore, three restoration methods are presented in this paper: firstly, the one is the least squares solution, the two is the zero-filling inverse solution by extending the coding matrix in the redundant coding state to a complete higher order Hadamard matrix, the three is sparse method. Secondly, the numerical and principle analysis shows that the inverse solution of zero-compensation has better robustness and is more suitable for engineering application; its conditional number, error expectation and covariance are better and more stable because it directly uses Hadamard matrix, which has good generalized orthogonality. Then, a real-time spectral reconstruction method is presented, which is based on inverse solution of zero-compensation. Finally, simulation analysis shows that spectral data could be destructed relative accuracy in the error condition; however, the effect of template noise and push error on reconstruction is much greater than that of detection error. Therefore, in addition to reducing the detection noise as much as possible, lower template noise and more accurate push controlling should be guaranteed specifically in engineering realization.

[1]  Chau-Jern Cheng,et al.  Coded aperture structured illumination digital holographic microscopy for superresolution imaging. , 2018, Optics letters.

[2]  Vishal M. Patel,et al.  Convolutional Sparse and Low-Rank Coding-Based Image Decomposition , 2017, IEEE Transactions on Image Processing.

[3]  Jiang Yue,et al.  Denoising analysis of spatial pixel multiplex coded spectrometer with Hadamard H-matrix , 2018 .

[4]  Jiangtao Wen,et al.  Hadamard Transform-Based Optimized HEVC Video Coding , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[5]  Qionghai Dai,et al.  Convolutional Sparse Coding for RGB+NIR Imaging , 2018, IEEE Transactions on Image Processing.

[6]  Man Lung Yiu,et al.  Piecewise linear regression-based single image super-resolution via Hadamard transform , 2018, Inf. Sci..

[7]  Dean Crnkovic,et al.  Orbit matrices of Hadamard matrices and related codes , 2018, Discret. Math..

[8]  Chaoqun Huang,et al.  Normal-inverse bimodule operation Hadamard transform ion mobility spectrometry. , 2018, Analytica chimica acta.

[9]  ian,et al.  Hadamard transform-based calibration method for programmable optical filters based on digital micro-mirror device , 2019 .

[10]  Xiang Li,et al.  Hadamard transform-based calibration method for programmable optical filters based on digital micro-mirror device. , 2018, Optics express.

[11]  Lin Wang,et al.  An Optimization-Oriented Algorithm for Sparse Signal Reconstruction , 2019, IEEE Signal Processing Letters.

[12]  Gou Koutaki,et al.  Hadamard Coding for Supervised Discrete Hashing , 2018, IEEE Transactions on Image Processing.

[13]  Henry Arguello,et al.  Compressive Coded Aperture Spectral Imaging: An Introduction , 2014, IEEE Signal Processing Magazine.

[14]  Carlos Hinojosa,et al.  Coded Aperture Design for Compressive Spectral Subspace Clustering , 2018, IEEE Journal of Selected Topics in Signal Processing.

[15]  Qionghai Dai,et al.  Dual-coded compressive hyperspectral imaging. , 2014, Optics letters.

[16]  Jin U. Kang,et al.  Real-time compressive sensing spectral domain optical coherence tomography. , 2014, Optics letters.