2019
DOI: 10.1038/s41598-019-40676-6
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Reconstruction of noisy images via stochastic resonance in nematic liquid crystals

Abstract: We employ nematic liquid crystals as the nonlinear medium to recover noisy images via stochastic resonance, in which nonlinear coupling allows signals to grow at the expense of noise. The process is theoretically analyzed and the cross-correlation is numerically calculated. It is found that the quality of output images is affected by the input noise intensity, the applied voltage and the correlation length of noise light. Noise-hidden images can be effectively recovered by optimizing these parameters. The resu… Show more

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Cited by 11 publications
(5 citation statements)
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References 26 publications
(39 reference statements)
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“…Very recently, another way to take benefit of SR in image processing by adjusting the system parameters has been addressed in a nonlinear optical system containing photorefractive or nematic liquid crystals [3235]. During this process of image enhancement, which uses modulation instability, the nonlinear coefficient of the crystal is controlled by an external voltage.…”
Section: Nonlinear Resonance In the Context Of Image Processing Visual Perception And Ditheringmentioning
confidence: 99%
See 1 more Smart Citation
“…Very recently, another way to take benefit of SR in image processing by adjusting the system parameters has been addressed in a nonlinear optical system containing photorefractive or nematic liquid crystals [3235]. During this process of image enhancement, which uses modulation instability, the nonlinear coefficient of the crystal is controlled by an external voltage.…”
Section: Nonlinear Resonance In the Context Of Image Processing Visual Perception And Ditheringmentioning
confidence: 99%
“…Lastly the performance of the detector is quantified by a measurement of similarity between the initial image I and the black and white detected image T . More precisely, we use the cross-covariance which constitutes a fairly appropriate measurement of similarity between two images [35,43,56]. This cross-covariance between image I and T is defined by CI,T=falsefalse⟨false(Ifalsefalse⟨Ifalsefalse⟩false)false(Tfalsefalse⟨Tfalsefalse⟩false)falsefalse⟩falsefalse⟨false(Ifalsefalse⟨Ifalsefalse⟩false)2falsefalse⟩falsefalse⟨false(Tfalsefalse⟨Tfalsefalse⟩false)2falsefalse⟩, where 〈.〉 corresponds to an average over the whole image, that is an average across all the image pixels.…”
Section: The Detector and Its Set-upmentioning
confidence: 99%
“…3(a-c) do not emphasize them since their transverse location and periodicity are wandering with time [34] and are washed out by the averaging process. Further study would be necessary to identify these short wavelength instabilities with noise sustained modulational instability [14] or other stochastic resonance behavior [38]. Although no focusing dispersive dam break flows are experimentally observed in our stochastic nonlinear medium, shock-like dynamics is still observable.…”
mentioning
confidence: 92%
“…As a result, the output power spectral density corresponding to the periodic excitation frequency may be greatly increased and exhibit a peak when SR occurs. Due to this constructive role of noise when SR is activated, SR has been explored in a variety of contexts, such as biomedical systems [18,19], image processing [20,21], and fault diagnosis in mechanical systems [22][23][24][25][26][27][28][29][30].…”
Section: Introductionmentioning
confidence: 99%