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| rdf:type
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| rdfs:seeAlso
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| Description
| - Image capturing is corrupted by numerous perturbing influences. These influences are divisible to time-invariant and temporal. The typical time-invariant influence is an image blurring, rising from various causes, that can be mathematically understood as deterministic 2D ISI channel or FSM (Finite state machine). Among temporal influences pertain especially noises on the other hand. There are four significant noise sources in the case of a camera with CCD (CMOS) sensor: photon noise (Poisson), thermal noise (Poisson), readout noise (Gaussian) and quantization noise.The photon noise cannot be compensated because its origin is located front of the lens. Therefore we will not take it into account. The thermal noise, readout noise and quantization noise together create one composite noise of the CCD/CMOS sensor that affects on the captured blurred image as the random IECS-ML channel. Such channel is biased by mean value and standard deviation of the readout noise, depending on the sensor readout rate, and mean value and squared standard deviation of the thermal noise, exponentially raising according to the sensor temperature. All mentioned influences can be eliminated by iterative detection network (IDN). Such system solves effectively the 2D MAP criterion through feedback process based on the exchange and precision of certain probability density functions (PDFs). Similar networks (simpler one-dimensional alternatives) have found utilization in the sphere of Turbo code detection. There will be discussed de facto theirs generalization to the two-dimensional form. The explanation begins by decomposition of the 2D MAP criterion that elucidates the essential principle of IDNs functioning. The necessary conditions for this decomposition will be defined too. Consequently, we focus closer to the IDNs dedicated for restoration of dichromatic images and using PDFs marginalization at the symbol level and symbol block level.
- Image capturing is corrupted by numerous perturbing influences. These influences are divisible to time-invariant and temporal. The typical time-invariant influence is an image blurring, rising from various causes, that can be mathematically understood as deterministic 2D ISI channel or FSM (Finite state machine). Among temporal influences pertain especially noises on the other hand. There are four significant noise sources in the case of a camera with CCD (CMOS) sensor: photon noise (Poisson), thermal noise (Poisson), readout noise (Gaussian) and quantization noise.The photon noise cannot be compensated because its origin is located front of the lens. Therefore we will not take it into account. The thermal noise, readout noise and quantization noise together create one composite noise of the CCD/CMOS sensor that affects on the captured blurred image as the random IECS-ML channel. Such channel is biased by mean value and standard deviation of the readout noise, depending on the sensor readout rate, and mean value and squared standard deviation of the thermal noise, exponentially raising according to the sensor temperature. All mentioned influences can be eliminated by iterative detection network (IDN). Such system solves effectively the 2D MAP criterion through feedback process based on the exchange and precision of certain probability density functions (PDFs). Similar networks (simpler one-dimensional alternatives) have found utilization in the sphere of Turbo code detection. There will be discussed de facto theirs generalization to the two-dimensional form. The explanation begins by decomposition of the 2D MAP criterion that elucidates the essential principle of IDNs functioning. The necessary conditions for this decomposition will be defined too. Consequently, we focus closer to the IDNs dedicated for restoration of dichromatic images and using PDFs marginalization at the symbol level and symbol block level. (en)
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| Title
| - 2D Iterative Detection Network Based Image Restoration: Principles, Applications and Performance Analysis
- 2D Iterative Detection Network Based Image Restoration: Principles, Applications and Performance Analysis (en)
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| skos:prefLabel
| - 2D Iterative Detection Network Based Image Restoration: Principles, Applications and Performance Analysis
- 2D Iterative Detection Network Based Image Restoration: Principles, Applications and Performance Analysis (en)
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| skos:notation
| - RIV/68407700:21230/12:00192389!RIV15-GA0-21230___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/68407700:21230/12:00192389
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| http://linked.open...riv/jazykVysledku
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| http://linked.open.../riv/klicovaSlova
| - Finite state machine; Image blurring; Photon noise; Thermal noise; Readout noise; Quantization noise; Random IECS-ML channel; Iterative detection network; 2D MAP criterion; Turbo code (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/mistoVydani
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| http://linked.open...vEdiceCisloSvazku
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| http://linked.open...i/riv/nazevZdroje
| - Image Restoration - Recent Advances and Applications
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| http://linked.open...in/vavai/riv/obor
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| http://linked.open...ichTvurcuVysledku
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| http://linked.open...v/pocetStranKnihy
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...iv/tvurceVysledku
| - Klíma, Miloš
- Kekrt, Daniel
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| number of pages
| |
| http://bibframe.org/vocab/doi
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| http://purl.org/ne...btex#hasPublisher
| - InTech - Open Access Company (InTech Europe)
|
| https://schema.org/isbn
| |
| http://localhost/t...ganizacniJednotka
| |
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