Blind deconvolution using the similarity of multiscales regularization for infrared spectrum. (16th October 2015)

• Record Type: Journal Article
• Authors: Huang, Tao; Liu, Hai; Zhang, Zhaoli; Liu, Sanyan; Liu, Tingting; Shen, Xiaoxuan; Zhang, Tianxu; Zhang, Jianfeng
• Is Part Of: Measurement science & technology. Volume 26:Number 11(2015:Nov.)
• Publication Date: 2015-10-16
• Subjects: infrared spectral data -- multiscales -- blind deconvolution -- spectral super-resolution -- digital signal processing; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/0957-0233/26/11/115502
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: Band overlap and random noise exist widely when the spectra are captured using an infrared spectrometer, especially since the aging of instruments has become a serious problem. In this paper, via introducing the similarity of multiscales, a blind spectral deconvolution method is proposed. Considering that there is a similarity between latent spectra at different scales, it is used as prior knowledge to constrain the estimated latent spectrum similar to pre-scale to reduce artifacts which are produced from deconvolution. The experimental results indicate that the proposed method is able to obtain a better performance than state-of-the-art methods, and to obtain satisfying deconvolution results with fewer artifacts. The recovered infrared spectra can easily extract the spectral features and recognize unknown objects.