Frequency selective signal extrapolation with applications to error concealment in image communication

Kaup, André; Meisinger, Katrin; Aach, Til

doi:10.1016/j.aeue.2005.03.015

Cited by 64 publications

(81 citation statements)

References 16 publications

(31 reference statements)

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“…It has been demonstrated in Ref. [23] that the CLEAN algorithm is a special case of a more general technique that is not limited by the use of a Fourier basis. Research in this direction is in progress.…”

Section: The Accuracy Of Cleanmentioning

confidence: 99%

Image reconstruction techniques applied to nuclear mass models

et al. 2010

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A new procedure is presented that combines well-known nuclear models with image reconstruction techniques. A color-coded image is built by taking the differences between measured masses and the predictions given by the different theoretical models. This image is viewed as part of a larger array in the (N, Z) plane, where unknown nuclear masses are hidden, covered by a "mask." We apply a suitably adapted deconvolution algorithm, used in astronomical observations, to "open the window" and see the rest of the pattern. We show that it is possible to improve significantly mass predictions in regions not too far from measured nuclear masses.

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Section: The Accuracy Of Cleanmentioning

confidence: 99%

Image reconstruction techniques applied to nuclear mass models

et al. 2010

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“…For selection of the domain in which image is interpolated, the common choices vary from direct interpolations over raw spatial domain pixels [8,18], to methods which operate on transformation of images using discrete cosine transform (DCT) [7] or discrete wavelet transforms (DWT) [17]. EC methods using combination of spatial and frequency domains [20,21,26].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, EC methods can be coded as stand-alone apps and deployed in networks or used as embedded applications on the receiver handsets/terminals; they do not require an international telecommunication union (ITU) approved standard. Therefore, spatial EC image gap restoration is the category of solution explored in this paper.At their core, image EC computation algorithms often involve two distinct processes: (a) image transformation and (b) extrapolation or interpolation of missing gaps.For selection of the domain in which image is interpolated, the common choices vary from direct interpolations over raw spatial domain pixels [8,18], to methods which operate on transformation of images using discrete cosine transform (DCT) [7] or discrete wavelet transforms (DWT) [17]. EC methods using combination of spatial and frequency domains [20,21,26].…”

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confidence: 99%

Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation

Langari

Vaseghi

Procházka

et al. 2016

IEEE Trans. on Image Process.

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-This paper presents improvements in image gap restoration through incorporation of edge-based directional interpolation within multi-scale pyramid transforms. Two types of image edges are reconstructed; (a) the local edges or textures, inferred from the gradients of the neighbouring pixels and (b) the global edges between image objects or segments, inferred using Canny detector. Through a process of pyramid transformation and downsampling, the image is progressively transformed into a series of reduced size layers until at the pyramid apex the gap size is one sample. At each layer an edge 'skeleton' image is extracted for edge-guided interpolation. The process is then reversed; from the apex, at each layer, the missing samples are estimated (an iterative method is used in the last stage of up-sampling), up-sampled and combined with the available samples of the next layer. Discrete cosine transform and a family of discrete wavelet transforms are utilized as alternatives for pyramid construction. Evaluations over a range of images, in regular and random loss pattern, at loss rates of up to 40%, demonstrate that the proposed method improves PSNR by 1 to 5 dB compared to a range of best published works.Index Terms-Error concealment, multi-scale DCT/DWT pyramid, edge detection, image gap recovery, packet loss concealment. I. INTRODUCTIONmage gap restoration have a wide range of applications that includes in-painting of missing or damaged segments in still images or the replacement of image data packet lost in transmission. Further examples of applications and environments where image gap restoration can be usefully applied are enhancement of distorted biomedical signals [1], restoration of archived damaged images [2] and packet loss concealment over internet protocol (IP) networks [3].A main current application of image gap restoration is packet loss concealment. Packet loss errors may occur due to network congestions or due to signal loss in mobile devices. IP networks are best-effort environments [4,5] where the packet delivery is not guaranteed. The rapid growth in demand for relatively high bandwidth image/video streaming applications over IP networks motivates the need for packet loss recovery and concealment in order to provide more reliable network services and more acceptable user experience [6].There are three broad approaches for mitigating the loss of quality in received images due to packet loss: (a) automatic request for retransmission (ARQ) of the lost packets, (b) error control via forward error correction (FEC) methods and (c) error concealment (EC) methods. The first method retransmits a copy of the damaged/lost packet and results in an increase in bandwidth and delay proportional to error rate [5]. This method can be used on request for retransmission in networks where there is an interaction between sender and receiver. The second category of methods, FEC, employs error correction coding to recover lost pixels from the received information. This implies that the pixel values in successive...

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“…In this work we applied the method proposed in [8] for error concealment in image and video communication. The main idea is to describe the acquired sinogram as a weighted linear combination of periodic basis functions.…”

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confidence: 99%

“…METHODS For a full description of the method applied, the reader is referred to [8]. For the sake of completeness a brief introduction is presented here.…”

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confidence: 99%

Frequency selective signal extrapolation for compensation of missing data in sinograms

Herraiz

España

Vicente

et al. 2008

2008 IEEE Nuclear Science Symposium Conference Record

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Abstract-We present a method to compensate for missing projection data in positron emission tomography (PET), which may result from gaps between adjacent detectors or from malfunctioning detectors. To prevent artifacts in the reconstruction when using Fourier rebinning (FORE) or analytical reconstruction algorithms, filling the data gaps is required. This new approach for sinogram data interpolation prior to reconstruction is based on a simple iterative freq uency selective signal extrapolation method initially proposed and successfully applied for error concealment in image and video communication. In this work the method has been improved taking advantage of well known restrictions in the allowed region of frequencies of the sinograms.We compare quantitatively the results of this technique with previously proposed gap filling procedures in both the sinograms and in their reconstructed images. The proposed technique outperforms those methods without requiring too much computational time.

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Frequency selective signal extrapolation with applications to error concealment in image communication

Cited by 64 publications

References 16 publications

Image reconstruction techniques applied to nuclear mass models

Image reconstruction techniques applied to nuclear mass models

Edge-Guided Image Gap Interpolation Using Multi-Scale Transformation

Frequency selective signal extrapolation for compensation of missing data in sinograms

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