Review of Inpainting Algorithms for Wireless Communication Application

Yatnalli, Veeramma; Shivaleelavathi, B. G.; Sudha, K

doi:10.48084/etasr.3547

Cited by 5 publications

(2 citation statements)

References 55 publications

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“…The values in Table 1 show that the three convolutional filters are indeed similar in terms of quantitative metrics [19,20]. In particular, the network trained with the Gaussian blur kernel is more correlated than the other two blur kernels.…”

Section: Methodsmentioning

confidence: 85%

Digital Image Restoration Based on Multicontour Batch Scanning

et al. 2022

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In order to explore the problem of digital image restoration, the authors propose a research on digital image restoration based on multicontour batch scanning. This method recommends key technical problems and solutions based on information represented by multicontour batch scans, exploring research in digital image restoration. Research has shown that the research on digital image restoration based on multicontour batch scanning is about 40% more efficient than traditional methods. Aiming at the new application of digital image inpainting, the application of image inpainting in image compression is studied in depth, and the technical principles of image inpainting and image compression are complemented.

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Section: Methodsmentioning

confidence: 85%

Digital Image Restoration Based on Multicontour Batch Scanning

et al. 2022

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“…However, there is another class of metasurface design that has been ignored, i.e., the inference of optical response from other correlated optical response. This is instrumental in a wide range of applications, for example, to extract the desired images, spectra, and material features from other easily accessible information with the advantages of low cost and easy measurement 25 . To distinguish it from forward and inverse designs, we term it the third-class metasurface design.…”

Section: Introductionmentioning

confidence: 99%

Correlating metasurface spectra with a generation-elimination framework

Chen,

Qian,

Zhang

et al. 2023

Nat Commun

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Inferring optical response from other correlated optical response is highly demanded for vast applications such as biological imaging, material analysis, and optical characterization. This is distinguished from widely-studied forward and inverse designs, as it is boiled down to another different category, namely, spectra-to-spectra design. Whereas forward and inverse designs have been substantially explored across various physical scenarios, the spectra-to-spectra design remains elusive and challenging as it involves intractable many-to-many correspondences. Here, we first dabble in this uncharted area and propose a generation-elimination framework that can self-orient to the best output candidate. Such a framework has a strong built-in stochastically sampling capability that automatically generate diverse nominations and eliminate inferior nominations. As an example, we study terahertz metasurfaces to correlate the reflection spectra from low to high frequencies, where the inaccessible spectra are precisely forecasted without consulting structural information, reaching an accuracy of 98.77%. Moreover, an innovative dimensionality reduction approach is executed to visualize the distribution of the abstract correlated spectra data encoded in latent spaces. These results provide explicable perspectives for deep learning to parse complex physical processes, rather than “brute-force” black box, and facilitate versatile applications involving cross-wavelength information correlation.

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