Efficient Blind Signal Reconstruction With Wavelet Transforms Regularization for Educational Robot Infrared Vision Sensing

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

98 Scopus Citations
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  • Tingting Liu
  • Hai Liu
  • Youfu Li
  • Zhaoli Zhang
  • Sanya Liu

Related Research Unit(s)


Original languageEnglish
Pages (from-to)384-394
Journal / PublicationIEEE/ASME Transactions on Mechatronics
Issue number1
Online published13 Sept 2018
Publication statusPublished - Feb 2019


Fourier transform infrared (FTIR) imaging spectrometers are often corrupted by the problems of band overlap and random noise during the infrared spectrum acquisition process. Such noise would degrade the quality of the acquired infrared spectrum, limiting the precision of the subsequent processing. In this paper, we present a novel blind reconstruction method with wavelet transform regularizations for infrared spectrum obtained from the aging instrument. Inspired by the finding that the wavelet coefficient distribution of the clean spectrum is sparser than that of the degraded spectrum, a blind reconstruction model for infrared spectrum is proposed in this paper to regularize the distribution of the degraded spectrum by total variation regularization. This method outperforms when suppressing random noise and preserving the spectral structure details. In addition, an effective optimization scheme is introduced in overcoming the issue of formulated optimization. The instrument response function and latent spectrum can be simultaneously estimated through the proposed method that can efficiently mitigate the effects caused by instrument degradation. Finally, extensive experiments on simulated and real noisy infrared spectra are carried out to demonstrate the superiority of the proposed method over the existing state-of-the-art ones. Thus, the reconstructed spectrum will better serve the feature extraction and educational robot infrared vision sensing in industrial applications. © 2018 IEEE.

Research Area(s)

  • FTIR imaging spectrometers, IEEE transactions, Image reconstruction, Imaging, instrumentation, Instruments, Mechatronics, mechatronics industry, optical data processing, robot vision, wavelet transforms

Citation Format(s)