Locally estimated heterogeneity property and its fuzzy filter application for deinterlacing

Jeon, Gwanggil; Anisetti, Marco; Wang, Lei; Damiani, Ernesto

doi:10.1016/j.ins.2016.03.016

Cited by 28 publications

(8 citation statements)

References 17 publications

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“…Given the luminance component (Y ) of a field, the amplitude spectrum noted A( f ) and the phase spectrum noted P( f ) are first evaluated as the real and the imaginary part of the two-dimensional Fourier transform of the luminance component, respectively: 2 (1)…”

Section: Visual Saliencymentioning

confidence: 99%

“…The process of deinterlacing involves converting a stream of interlaced frames within a video sequence to progressive frames [1], to ensure their playback on nowadays progressive devices.Such video processing has been widely studied in the recent literature [2][3][4][5][6][7][8], as the interlaced video format is still preferred for the acquisition systems when high-fidelity motion accuracy is needed. Deinterlacing requires the display device to buffer one or more fields and recombine them to a full progressive frame.…”

Section: Introductionmentioning

confidence: 99%

“…Spatial deinterlacers [2,4,7,9] use the information from the current field to interpolate the missing field lines. The most common types of spatial deinterlacing methods are line averaging and directional spatial interpolation.…”

Section: Introductionmentioning

confidence: 99%

“…A way to exploit fuzzy reasoning to reinforce contours for improving an edge-adaptive deinterlacing algorithm without an excessive increase in computational complexity is discussed in [14]. Another approach for fuzzy logic deinterlacing is to use a fuzzy-bilateral filtering method which considers the range and domain filters based on a fuzzy metric [2,8].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

An HVS-inspired video deinterlacer based on visual saliency

Aggarwal

Trocan

Coudoux

2016

Vietnam J Comput Sci

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Video deinterlacing is a technique wherein the interlaced video format is converted into progressive scan format for nowadays display devices. In this paper, a spatial saliency-guided motion-compensated deinterlacing method is proposed which accounts for the properties of the Human Visual System (HVS): our algorithm classifies the field according to its texture and viewer's region of interest and adapts the motion estimation and compensation, as well as the saliency-guided interpolation to ensure high-quality frame reconstruction. Two different saliency models, namely the graph-based visual saliency (GBVS) model and the spectral residual visual saliency (SRVS) model, have been studied and compared in terms of visual quality performances as well as computational complexity. The experimental results on a great variety of video test sequences show significant improvement of reconstructed video quality with the GBVS-based proposed method compared to classical motion-compensated and adaptive deinterlacing techniques, with up to 4.5 dB gains in terms of PSNR. Simulations also show that the SRVS-based deinterlacing process can result to significant reductions of complexity (up to 25 times a decrease of the computation time compared with the GBVSbased method) at the expense of a PSNR decrease.

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Section: Visual Saliencymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An HVS-inspired video deinterlacer based on visual saliency

Aggarwal

Trocan

Coudoux

2016

Vietnam J Comput Sci

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show abstract

“…The desired demosaicking method must take advantage of high visual quality images with low complexity. To accomplish this task, many researchers have been studied various demosaicking methods [8][9][10][11][12][13][14][15]. Most existent demosaicking algorithms usually concentrate upon how to precisely discover the minimum variation direction in the mosaicked CFA image.…”

Section: Introductionmentioning

confidence: 99%

Image enhancement in embedded devices for internet of things

Jeon

Pasupa

Anisetti

et al. 2019

Concurrency and Computation

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This paper proposes a new color interpolation method which can be used in embedded devices for IoT system. In this work, we use regression approach for generating and designing filters to restore color image. The filters are designed with four sizes, 5x5 training filter, 7x7 training filter, 9x9 training filter, and 11x11 training filter. The obtained filters are tested in 25 LC dataset to assess the performance.Experimental results inform that the proposed filters provide outstanding performance when they are compared with conventional methods. As compared with the other methods, the proposed filters produce the best average interpolation performance both objectively and visually.

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Improving resolution of medical images with deep dense convolutional neural network

Wei

Jeon

et al. 2018

Concurrency and Computation

Self Cite

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Doctors always desire high-resolution medical images to have accurate diagnosis. Super-resolution (SR) is a technology that can improve the resolution of medical images. Convolutional neural network (CNN)-based SR methods have achieved desired performance in natural images. In this paper, we apply a deep dense SR (DDSR) convolutional neural networks model to two types of medical images, including Computerized Tomography (CT) images and Magnetic Resonance imaging (MRI) images. This network densely connects every hidden layer to learn high-level features, which was first proposed for object recognition. A set of medical images is used for experiments. We compare the performance of DDSR with three state-of-the-art SR network models, including SR Convolutional Neural Network (SRCNN), Fast SR Convolutional Neural Network (FSRCNN), and Very Deep SR Convolutional Neural Network (VDSR). Both the objective indices and subjective evaluations are used for comparison. The results show that the proposed network has better performances both on CT and MRI images. KEYWORDS deep dense convolutional neural network, super-resolution, medical image 1 Concurrency Computat Pract Exper. 2020;32:e5084. wileyonlinelibrary.com/journal/cpe

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Locally estimated heterogeneity property and its fuzzy filter application for deinterlacing

Cited by 28 publications

References 17 publications

An HVS-inspired video deinterlacer based on visual saliency

An HVS-inspired video deinterlacer based on visual saliency

Image enhancement in embedded devices for internet of things

Improving resolution of medical images with deep dense convolutional neural network

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