Frequency selective extrapolation with residual filtering for image error concealment

• URL: http://arxiv.org/abs/2205.07476v1
• Date: Mon, 16 May 2022 06:59:36 GMT
• Title: Frequency selective extrapolation with residual filtering for image error concealment
• Authors: J\'an Koloda, J\"urgen Seiler, Andr\'e Kaup, Victoria S\'anchez, Antonio M. Peinado
• Abstract summary: The purpose of signal extrapolation is to estimate unknown signal parts from known samples. We propose a modified frequency selective extrapolation (FSE) that takes into account the low-pass behaviour of natural images.
• Score: 2.3484130340004326
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The purpose of signal extrapolation is to estimate unknown signal parts from known samples. This task is especially important for error concealment in image and video communication. For obtaining a high quality reconstruction, assumptions have to be made about the underlying signal in order to solve this underdetermined problem. Among existent reconstruction algorithms, frequency selective extrapolation (FSE) achieves high performance by assuming that image signals can be sparsely represented in the frequency domain. However, FSE does not take into account the low-pass behaviour of natural images. In this paper, we propose a modified FSE that takes this prior knowledge into account for the modelling, yielding significant PSNR gains.

Related papers

• Provably Robust Score-Based Diffusion Posterior Sampling for Plug-and-Play Image Reconstruction [31.503662384666274]
  In science and engineering, the goal is to infer an unknown image from a small number of measurements collected from a known forward model describing certain imaging modality. Motivated Score-based diffusion models, due to its empirical success, have emerged as an impressive candidate of an exemplary prior in image reconstruction.
  arXiv  Detail & Related papers  (2024-03-25T15:58:26Z)
• Misalignment-Robust Frequency Distribution Loss for Image Transformation [51.0462138717502]
  This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution. We introduce a novel and simple Frequency Distribution Loss (FDL) for computing distribution distance within the frequency domain. Our method is empirically proven effective as a training constraint due to the thoughtful utilization of global information in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-28T09:27:41Z)
• FS-BAND: A Frequency-Sensitive Banding Detector [55.59101150019851]
  Banding artifact, as known as staircase-like contour, is a common quality annoyance that happens in compression, transmission, etc. We propose a no-reference banding detection model to capture and evaluate banding artifacts, called the Frequency-Sensitive BANding Detector (FS-BAND) Experimental results show that the proposed FS-BAND method outperforms state-of-the-art image quality assessment (IQA) approaches with higher accuracy in banding classification task.
  arXiv  Detail & Related papers  (2023-11-30T03:20:42Z)
• Denoising Diffusion Models for Plug-and-Play Image Restoration [135.6359475784627]
  This paper proposes DiffPIR, which integrates the traditional plug-and-play method into the diffusion sampling framework. Compared to plug-and-play IR methods that rely on discriminative Gaussian denoisers, DiffPIR is expected to inherit the generative ability of diffusion models.
  arXiv  Detail & Related papers  (2023-05-15T20:24:38Z)
• Representing Noisy Image Without Denoising [91.73819173191076]
  Fractional-order Moments in Radon space (FMR) is designed to derive robust representation directly from noisy images. Unlike earlier integer-order methods, our work is a more generic design taking such classical methods as special cases.
  arXiv  Detail & Related papers  (2023-01-18T10:13:29Z)
• SAR Despeckling using a Denoising Diffusion Probabilistic Model [52.25981472415249]
  The presence of speckle degrades the image quality and adversely affects the performance of SAR image understanding applications. We introduce SAR-DDPM, a denoising diffusion probabilistic model for SAR despeckling. The proposed method achieves significant improvements in both quantitative and qualitative results over the state-of-the-art despeckling methods.
  arXiv  Detail & Related papers  (2022-06-09T14:00:26Z)
• FreqNet: A Frequency-domain Image Super-Resolution Network with Dicrete Cosine Transform [16.439669339293747]
  Single image super-resolution(SISR) is an ill-posed problem that aims to obtain high-resolution (HR) output from low-resolution (LR) input. Despite the high peak signal-to-noise ratios(PSNR) results, it is difficult to determine whether the model correctly adds desired high-frequency details. We propose FreqNet, an intuitive pipeline from the frequency domain perspective, to solve this problem.
  arXiv  Detail & Related papers  (2021-11-21T11:49:12Z)
• Anomaly Detection By Autoencoder Based On Weighted Frequency Domain Loss [1.0312968200748116]
  In image anomaly detection, Autoencoders are the popular methods that reconstruct the input image that might contain anomalies and output a clean image with no abnormalities. In this paper, we show our method's superiority over the conventional Autoencoder methods by comparing it with AUROC on the MVTec AD dataset.
  arXiv  Detail & Related papers  (2021-05-21T09:10:36Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• Scale factor point spread function matching: Beyond aliasing in image resampling [4.81150027600776]
  Imaging devices exploit the Nyquist-Shannon sampling theorem to avoid both aliasing and redundant oversampling by design. In medical image resampling, images are considered as continuous functions, are warped by a spatial transformation, and are then sampled on a regular grid. This paper shows that this oversight introduces artefacts, including aliasing, that can lead to important bias in clinical applications.
  arXiv  Detail & Related papers  (2021-01-16T11:40:58Z)
• An Effective Anti-Aliasing Approach for Residual Networks [27.962502376542588]
  Frequency aliasing is a phenomenon that may occur when sub-sampling any signal, such as an image or feature map, causing distortion in the sub-sampled output. We show that we can mitigate this effect by placing non-trainable blur filters and using smooth activation functions at key locations. These simple architectural changes lead to substantial improvements in out-of-distribution generalization on both image classification under natural corruptions on ImageNet-C and few-shot learning on Meta-Dataset.
  arXiv  Detail & Related papers  (2020-11-20T22:55:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.