Robust infrared spectral deconvolution for image segmentation with spatial information regularization

Shao, Guangpu; Wang, Tianjiang

doi:10.1016/j.infrared.2019.103011

Cited by 5 publications

(3 citation statements)

References 57 publications

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“…The infrared spectrometer equipment and Fourier-optical idea are used to calculate the factor unfold feature. It can estimate the factor unfold feature simultaneously via way of means of introducing adaptive overall version and constraint regularization [100]. For infrared photograph segmentation, a completely unique convolutional neural community turned into offered that may resolve the problems of movement blur, low resolution, and random noise.…”

Section: Physics and Astronomymentioning

confidence: 99%

Image Segmentation Using Hybrid Optimization Algorithms: Review

Hemeida¹,

Yahya

Al-Sanary

2022

Aswan University Journal of Sciences and Technology

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Image segmentation, often based on the properties of image pixels, is a widely used method in digital image processing and analysis to divide an image into multiple parts or areas. The main goal of image segmentation is to simplify the image for easier analysis. It would be very difficult to implement computer vision without performing image segmentation. An important component of computer vision is image segmentation, which has numerous commercial applications. Google and others Image-based search engines use image segmentation algorithms to identify the objects in your image and match their results to the relevant images they find to present you with search results.

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Section: Physics and Astronomymentioning

confidence: 99%

Image Segmentation Using Hybrid Optimization Algorithms: Review

Hemeida¹,

Yahya

Al-Sanary

2022

Aswan University Journal of Sciences and Technology

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show abstract

“…2 Many spectral resolution enhancement methods have been developed by researchers to address the problem of low spectral resolution. [3][4][5][6][7][8] Among these, digital signal processing technology, such as linear prediction technology, is critical for improving spectral resolution. 9,10 Linear prediction technology improves spectral resolution by making a model of the interference signal and using its autoregressive (AR) model to extrapolate the interference signal to a longer optical path difference.…”

Section: Introductionmentioning

confidence: 99%

“…2 Many spectral resolution enhancement methods have been developed by researchers to address the problem of low spectral resolution. 3–8 Among these, digital signal processing technology, such as linear prediction technology, is critical for improving spectral resolution. 9,10…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation in Spectral Resolution Enhancement Based on Forward–Backward Linear Prediction Total Least Square Method

Qin

Han

et al. 2023

Appl Spectrosc

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In a Fourier transform infrared (IR) spectrometer, the Michelson interference signal extrapolation method based on linear prediction is often used to improve spectral resolution. In this method, an autoregressive (AR) model is established for the Michelson interference signal in the spectrometer. Once the AR model parameters are determined, the AR process is predictable. The interference signal can be used to figure out the AR model's parameters. Based on this, the AR model can be used to extrapolate the interference signal to improve the spectral resolution. In this paper, the forward–backward linear prediction total least squares (FB-TLS) method is proposed to estimate the parameters of the AR model. The parameters that are estimated are used to improve the IR spectral resolution. By simulating different order and signal-to-noise ratio situations, the effects of the Burg, the least square, and the FB-TLS parameter estimation methods on spectral resolution enhancement are studied. The simulation results demonstrate that the FB-TLS parameter estimation method can effectively suppress noise and avoid spurious peaks. The experimental results demonstrate that the FB-TLS parameter estimation method is effective for spectral resolution enhancement technology based on linear prediction. When the FB-TLS method is used to enhance NH3 IR spectral resolution from 2 cm−1 to 1 cm−1, the spectral prediction error in the NH3 characteristic band is only 0.21% compared with the measured NH3 spectrum, whose spectral resolution is 1 cm−1.

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Fast facial smile detection using convolutional neural network in an intelligent working environment

Liu

et al. 2020

Infrared Physics & Technology

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Robust infrared spectral deconvolution for image segmentation with spatial information regularization

Cited by 5 publications

References 57 publications

Image Segmentation Using Hybrid Optimization Algorithms: Review

Image Segmentation Using Hybrid Optimization Algorithms: Review

Parameter Estimation in Spectral Resolution Enhancement Based on Forward–Backward Linear Prediction Total Least Square Method

Fast facial smile detection using convolutional neural network in an intelligent working environment

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