Inverse Filtering Method for Super-Resolution Digital Imaging

AbstractObjectives:This study aimed to analyse vocal performance and to investigate the nature of the neoglottal sound source in patients who had undergone supracricoid laryngectomy with cricohyoidoepiglottopexy, using a high-speed digital imaging system.Methods:High-speed digital imaging analysis of neoglottal kinetics was performed in two patients who had undergone supracricoid laryngectomy with cricohyoidoepiglottopexy; laryngotopography, inverse filtering analysis and multiline kymography were also undertaken.Results:In case one, laryngotopography demonstrated two vibrating areas: one matched with the primary (i.e. fundamental) frequency (75 Hz) and the other with the secondary frequency (150 Hz) at the neoglottis. In case two, laryngotopography showed two vibrating areas matched with the fundamental frequency (172 Hz) at the neoglottis. The interaction between the two areas was considered to be the sound source in both patients. The waveform of the estimated volume flow at the neoglottis, obtained by inverse filtering analysis, corresponded well to the neoglottal vibration patterns derived by multiline kymography. These findings indicated that the specific sites identified at the neoglottis by the present method were likely to be the sound source in each patient.Conclusions:High-speed digital imaging analysis is effective in locating the sites responsible for voice production in patients who have undergone supracricoid laryngectomy with cricohyoidoepiglottopexy. This is the first study to clearly identify the neoglottal sound source in such patients, using a high-speed digital imaging system.

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Super-Resolution Surface Mapping for Scanning Radar: Inverse Filtering Based on the Fast Iterative Adaptive Approach

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2017.2743263 ◽

2018 ◽

Vol 56 (1) ◽

pp. 127-144 ◽

Cited By ~ 27

Author(s):

Yongchao Zhang ◽

Yin Zhang ◽

Wenchao Li ◽

Yulin Huang ◽

Jianyu Yang

Keyword(s):

Super Resolution ◽

Inverse Filtering ◽

Surface Mapping ◽

Adaptive Approach ◽

Iterative Adaptive Approach

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Remote Sensing Image Super-resolution Based on Sparse Representation

MATEC Web of Conferences ◽

10.1051/matecconf/201823202037 ◽

2018 ◽

Vol 232 ◽

pp. 02037

Author(s):

Fuzhen Zhu ◽

Yue Liu ◽

Xin Huang ◽

Haitao Zhu

Keyword(s):

Remote Sensing ◽

Sparse Representation ◽

Super Resolution ◽

Remote Sensing Image ◽

Training Sample ◽

Mean Value ◽

Image Feature ◽

Edge Information ◽

Filtering Method ◽

Image Super Resolution

In order to obtain higher resolution remote sensing images with more details, an improved sparse representation remote sensing image super-resolution reconstruction(SRR) algorithm is proposed. First, remote sensing image is preprocessed to obtain the required training sample image; then, the KSVD algorithm is used for dictionary training to obtain the high-low resolution dictionary pairs; finally, the image feature extraction block is represented, which is improved by using adaptive filtering method. At the same time, the mean value filtering method is used to improve the super-resolution reconstruction iterative calculation. Experiment results show that, compared with the most advanced sparse representation super-resolution algorithm, the improved sparse representation super-resolution method can effectively avoid the loss of edge information of SRR image and obtain a better super-resolution reconstruction effect. The texture details are more abundant in subjective vision, the PSNR is increased about 1 dB, and the structure similarity (SSIM) is increased about 0.01.

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