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Weighted MSE based spatially adaptive BM3D

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Details

Original languageEnglish
Title of host publication2017 25th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages733-737
Number of pages5
ISBN (Electronic)978-0-9928626-7-1
ISBN (Print)978-1-5386-0751-0
DOIs
Publication statusPublished - 1 Aug 2017
Publication typeA4 Article in a conference publication
EventEuropean Signal Processing Conference -
Duration: 1 Jan 1900 → …

Publication series

Name
ISSN (Electronic)2076-1465

Conference

ConferenceEuropean Signal Processing Conference
Period1/01/00 → …

Abstract

Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (

Keywords

  • image denoising, image filtering, mean square error methods, neural nets, BM3D filter modification, MSE modification, filtered images, filtering efficiency, homogeneous regions, human perception, image datasets, image filters, image quality metric, neural network, noise suppression, spatially adaptive BM3D, visual quality, wMSE, weighted MSE, Biological neural networks, Filtering, Noise measurement, Signal processing, Visualization, BM3D, image visual quality assessment, neural networks

Publication forum classification

Field of science, Statistics Finland