On Prediction of Image Denoising Expedience Using Neural Networks

Rubel, Andrii; Rubel, Oleksii

doi:10.1109/infocommst.2018.8632050

Cited by 4 publications

(4 citation statements)

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“…Thus,thesmartgovernmentvisionaimsatimprovinggovernmentservices(i.e.,bymakingthem quick, measurable, affordable and sustainable) and at enabling collaboration among government entities,nonprofitagencies,privatebusinesses,andthepublic.Thiscollaborationresultsinintegration and improvement of previously distinct processes, systems and policy domains (Howard, 2013). Similarly, Rubel (2016) defines smart government as: "the implementation of a set of business processes and underlying information technology capabilities that enable information to flow seamlesslyacrossgovernmentagenciesandprogramstobecomeintuitiveinprovidinghighquality citizenservicesacrossallgovernmentprogramsandactivitydomains. "…”

Section: Towards Smart Governmentmentioning

confidence: 99%

Legal Logistics

Netten

Braak

Bargh

et al. 2018

International Journal of E-Planning Research

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Thispaperpresentsaframeworktoprovideaunifiedviewtowardsthevisionsofsmartandopen justice.Theframework,coinedasLegalLogistics,aimsatunifyingandembodyingdifferentdatacentricservicesthatexploitavailableandrelevantdataforsupportingandenhancingthelegitimacy andefficiencyoflegalsystems.Assuch,theframeworkspecifiesthescopeofdata-centricservicesin legalsystems.Suchaunifiedviewofdata-centricservices,enablestheauthorstorelatetheseservices toeachotherandtotheiroperationalcontext,andbetterstreamlinedata-centricbasedinnovations inlegalsystems.Twodata-centricservicesrealizedfortheDutchcriminaljusticesystemwillbe discussed. These services innovatively integrate different datasets in order to give some insights aboutthewell-functioningandbudgetaryneedsoftheDutchlegalsystem.Theseusecasesprimarily illustratethetypicalchallengesandbenefitsofrealizingthevisionofsmartjustice.Secondarily,they illustratetherelevancyandusefulnessoftheembodyingLegalLogisticsframework.

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Section: Towards Smart Governmentmentioning

confidence: 99%

Legal Logistics

Netten

Braak

Bargh

et al. 2018

International Journal of E-Planning Research

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“…The task of filtering efficiency prediction has attracted sufficient attention of researchers in recent ten years [21][22][23][24][34][35][36][37][38]. It has been shown in [21] that, having a noise-free image, it is possible to determine what is a potential output MSE for nonlocal filtering of this image if it is corrupted by additive white Gaussian noise (AWGN) with a given standard deviation or variance.…”

Section: Introductionmentioning

confidence: 99%

“…We have studied other methods to filtering efficiency prediction [24,[34][35][36][37][38]. Initial assumptions are the following:…”

Section: Introductionmentioning

confidence: 99%

“…As shown in [37], a prediction of metric values for original (noisy) images is possible by processing sixteen input parameters by trained neural network even if a noise variance is unknown. Similarly, an improvement of metric values can be predicted using the trained NN under the condition that noise variance is unknown where input parameters relate to statistics of DCT coefficients in four areas for 8 × 8 pixel blocks.…”

Section: Introductionmentioning

confidence: 99%

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NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

2019

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Images acquired by synthetic aperture radars are degraded by speckle that prevents efficient extraction of useful information from radar remote sensing data. Filtering or despeckling is a tool often used to improve image quality. However, depending upon image and noise properties, the quality of improvement can vary. Besides, a quality can be characterized by different criteria or metrics, where visual quality metrics can be of value. For the case study of discrete cosine transform (DCT)based filtering, we show that improvement of radar image quality due to denoising can be predicted in a simple and fast way, especially if one deals with particular type of radar data such as images acquired by Sentinel-1. Our approach is based on application of a trained neural network that, in general, might have a different number of inputs (features). We propose a set of features describing image and noise statistics from different viewpoints. From this set, that contains 28 features, we analyze different subsets and show that a subset of the 13 most important and informative features leads to a very accurate prediction. Test image generation and network training peculiarities are discussed. The trained neural network is then tested using different verification strategies. The results of the network application to test and real-life radar images are presented, demonstrating good performance for a wide set of quality metrics.

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Neural Network-based Prediction of Visual Quality for Noisy Images

Rubel

2019

2019 IEEE 15th International Conference on the Experience of Designing and Application of CAD Systems (CADSM)

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On Prediction of Image Denoising Expedience Using Neural Networks

Cited by 4 publications

References 48 publications

Legal Logistics

Legal Logistics

NN-Based Prediction of Sentinel-1 SAR Image Filtering Efficiency

Neural Network-based Prediction of Visual Quality for Noisy Images

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