Encapsulation Material for Retinal Prosthesis With Photodetectors or Photovoltaics

Baek, Changhoon; Seo, Jong-Mo

doi:10.1109/jsen.2021.3132042

Cited by 6 publications

(3 citation statements)

References 35 publications

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“…The sampling, processing, and application of neuromorphic vision is another important field of neuromorphic engineering (Wang T. et al, 2022). This activity validates the computational neuroscience's visual brain model (Baek and Seo, 2022) and is a useful method for examining human intellect. The development of neuromorphic vision sensors is still in its infancy, and more investigation and study are still required to match or even surpass the human visual system's capacity for perception in intricately interconnected settings (Chen J. et al, 2023).…”

Section: Introductionmentioning

confidence: 52%

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

Li,

Sun

2023

Front. Mater.

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Introduction: Advances in machine vision and mobile electronics will be accelerated by the creation of sophisticated optoelectronic vision sensors that allow for sophisticated picture recognition of visual information and data pre-processing. Several new types of vision sensors have been devised in the last decade to solve these drawbacks, one of which is neuromorphic vision sensors, which have exciting qualities such as high temporal resolution, broad dynamic range, and low energy consumption. Neuromorphic sensors are inspired by the working principles of biological sensory neurons and would be useful in telemedicine, health surveillance, security monitoring, automatic driving, intelligent robots, and other applications of the Internet of Things.Methods: This paper provides a comprehensive review of various state-of-the-art AI vision sensors and frameworks.Results: The fundamental signal processing techniques deployed and the associated challenges were discussed.Discussion: Finally, the role of vision sensors in computer vision is also discussed.

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Section: Introductionmentioning

confidence: 52%

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

Li,

Sun

2023

Front. Mater.

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“…Typically polymers like polydimethylsiloxane (PDMS) (Lötters et al, 1997), polymethyl methacrylate (PMMA) (Suaning et al, 2014), polyimide (PI) (Ghosh, 1996) and cyclic olefin copolymer (COC) (Baek et al, 2020) and other such polymers are good common encapsulation materials for implantable devices. In this device, PDMS, colorless transparent polyimide (CPI) (Baek and Seo, 2022) and COC are superior to other materials in terms of optical transparency. Considering the excellent encapsulation properties of COC due to its thermoplasticity and its ability to be fabricated into microlens arrays (Baek et al, 2021), we believe that COC is worthy of further in-depth study to further advance the field of optical retinal prostheses.…”

Section: "Eye In Eye" Based On Reflectionmentioning

confidence: 99%

The mechanism of human color vision and potential implanted devices for artificial color vision

Zhang,

Zhao

et al. 2024

Front. Neurosci.

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Vision plays a major role in perceiving external stimuli and information in our daily lives. The neural mechanism of color vision is complicated, involving the co-ordinated functions of a variety of cells, such as retinal cells and lateral geniculate nucleus cells, as well as multiple levels of the visual cortex. In this work, we reviewed the history of experimental and theoretical studies on this issue, from the fundamental functions of the individual cells of the visual system to the coding in the transmission of neural signals and sophisticated brain processes at different levels. We discuss various hypotheses, models, and theories related to the color vision mechanism and present some suggestions for developing novel implanted devices that may help restore color vision in visually impaired people or introduce artificial color vision to those who need it.

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“…F OR many years, the application of retinal image in medical imaging was studied by researchers, i.e., automatic cataract classification [1], diabetic retinopathy [2], cholesterol level detection [3]- [5], automatic vessel segmentation [6], and retinal prosthesis [7]. These applications demand a lot of retinal images to analyze and require efficient compression techniques for retinal image storage.…”

Section: Introductionmentioning

confidence: 99%

Compressive Sampling of Color Retinal Image Using Spread Spectrum Fourier Sampling and Total Variant

et al. 2022

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This paper proposes compressive sampling (CS) framework for color retinal image (CRI) compression, which relies on spread spectrum Fourier sampling (SSFS) and total variant (TV)-based reconstruction method with three loop of RGB color space, referred to as RGB-TV. In CS, two steps of process are performed, i.e., compression and CS reconstruction. In compression steps, SFFS is performed to get compressed signal from original CRI with a high compression ratio (CR). While in CS reconstruction, TV-norm and TV proximal operator are exploited for problem optimization to recover original CRI from a compressed signal. In addition, signal-to-noise ratio (SNR), structural similarity (SSIM), and processing time are investigated for the performance metrics of the proposed RGB-TV. The computer simulation result shows that the proposed RGB-TV with a set of CRI of size 512 by 512 can compressed until CR = 10 which obtains mean SNR 22 dB, SSIM 0.84, and processing time 2.2 seconds. INDEX TERMSColor retinal image, compressive sampling (CS), RGB, spread spectrum (SS), total variant (TV).

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Encapsulation Material for Retinal Prosthesis With Photodetectors or Photovoltaics

Cited by 6 publications

References 35 publications

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

Artificial intelligence-based spatio-temporal vision sensors: applications and prospects

The mechanism of human color vision and potential implanted devices for artificial color vision

Compressive Sampling of Color Retinal Image Using Spread Spectrum Fourier Sampling and Total Variant

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