Signal and Loss Geometry Aware Frequency Selective Extrapolation for Error Concealment

Genser, Nils; Seiler, Jurgen; Schilling, Franz; Kaup, André

doi:10.1109/pcs.2018.8456259

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…Another method that can be used for resampling and reconstruction is the model-based Frequency-Selective Mesh-to-Grid Resampling (FSMR) [6]. It originates from frequencyselective reconstruction [12], which was developed over the last years [13], [14]. It is designed to solve quarter sampling problems and thus can be seen as a representative of the irregular sampling group.…”

Section: State-of-the-artmentioning

confidence: 99%

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Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting

Heimann

Genser

Kaup

2020

2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)

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Many applications in image processing require resampling of arbitrarily located samples onto regular grid positions. This is important in frame-rate up-conversion, superresolution, and image warping among others. A state-of-the-art high quality model-based resampling technique is frequencyselective mesh-to-grid resampling which requires pre-estimation of key points. In this paper, we propose a new key point agnostic frequency-selective mesh-to-grid resampling that does not depend on pre-estimated key points. Hence, the number of data points that are included is reduced drastically and the run time decreases significantly. To compensate for the key points, a spectral weighting function is introduced that models the optical transfer function in order to favor low frequencies more than high ones. Thereby, resampling artefacts like ringing are supressed reliably and the resampling quality increases. On average, the new AFSMR is conceptually simpler and gains up to 1.2 dB in terms of PSNR compared to the original mesh-to-grid resampling while being approximately 14.5 times faster.

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Section: State-of-the-artmentioning

confidence: 99%

“…Moreover, the presented AFSMR can also be reformulated according to [13], resulting in an additional significant speed-up. A further acceleration of the processing time can be achieved if the signal characteristics are taken into account [14].…”

Section: B Evaluationmentioning

confidence: 99%

Key Point Agnostic Frequency-Selective Mesh-to-Grid Image Resampling using Spectral Weighting

Heimann

Genser

Kaup

2020

2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP)

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“…The Naivier–Stokes (NS) method [ 37 ] works by assessing many similarities between 2D image quantities (intensity, isophote direction, smoothness, anisotropic diffusion) and fluid-related ones (stream function, fluid velocity, vorticity, fluid viscosity). Frequency Selective Reconstruction (FSR) [ 38 ] is a block-based algorithm that approximates the known samples by a weighted superposition of Fourier basis functions to estimate the lost pixels [ 39 ]; in some ways, it follows the reconstruction paradigm in CS.…”

Section: Introductionmentioning

confidence: 99%

Automatic Differentiation for Inverse Problems in X-ray Imaging and Microscopy

Guzzi

Gianoncelli

Billè

et al. 2023

Life

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Computational techniques allow breaking the limits of traditional imaging methods, such as time restrictions, resolution, and optics flaws. While simple computational methods can be enough for highly controlled microscope setups or just for previews, an increased level of complexity is instead required for advanced setups, acquisition modalities or where uncertainty is high; the need for complex computational methods clashes with rapid design and execution. In all these cases, Automatic Differentiation, one of the subtopics of Artificial Intelligence, may offer a functional solution, but only if a GPU implementation is available. In this paper, we show how a framework built to solve just one optimisation problem can be employed for many different X-ray imaging inverse problems.

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