Wavelet Burst Accumulation for turbulence mitigation

• URL: http://arxiv.org/abs/2410.22802v1
• Date: Wed, 30 Oct 2024 08:31:48 GMT
• Title: Wavelet Burst Accumulation for turbulence mitigation
• Authors: Jerome Gilles, Stanley Osher,
• Abstract summary: We investigate the extension of the recently proposed weighted Fourier burst accumulation (FBA) method into the wavelet domain. The purpose of the method is to reconstruct a clean and sharp image from a sequence of blurred frames.
• Score: 0.0
• License:
• Abstract: In this paper, we investigate the extension of the recently proposed weighted Fourier burst accumulation (FBA) method into the wavelet domain. The purpose of FBA is to reconstruct a clean and sharp image from a sequence of blurred frames. This concept lies in the construction of weights to amplify dominant frequencies in the Fourier spectrum of each frame. The reconstructed image is then obtained by taking the inverse Fourier transform of the average of all processed spectra. In this paper, we first suggest to replace the rigid registration step used in the original algorithm by a non-rigid registration in order to be able to process sequences acquired through atmospheric turbulence. Second, we propose to work in a wavelet domain instead of the Fourier one. This leads us to the construction of two types of algorithms. Finally, we propose an alternative approach to replace the weighting idea by an approach promoting the sparsity in the used space. Several experiments are provided to illustrate the efficiency of the proposed methods.

Related papers

• The Empirical Watershed Wavelet [0.0]
  In this paper, we provide theoretical results that permits us to build 2D empirical wavelet filters based on an arbitrary partitioning of the frequency domain. We also propose an algorithm to detect such partitioning from an image spectrum by combining a scale-space representation to estimate the position of dominant harmonic modes and a watershed transform.
  arXiv  Detail & Related papers  (2024-10-24T22:36:43Z)
• F2former: When Fractional Fourier Meets Deep Wiener Deconvolution and Selective Frequency Transformer for Image Deblurring [8.296475046681696]
  We propose a novel approach based on the Fractional Fourier Transform (FRFT), a unified spatial-frequency representation. We show that the performance of our proposed method is superior to other state-of-the-art (SOTA) approaches.
  arXiv  Detail & Related papers  (2024-09-03T17:05:12Z)
• FourierMamba: Fourier Learning Integration with State Space Models for Image Deraining [71.46369218331215]
  Image deraining aims to remove rain streaks from rainy images and restore clear backgrounds. We propose a new framework termed FourierMamba, which performs image deraining with Mamba in the Fourier space.
  arXiv  Detail & Related papers  (2024-05-29T18:58:59Z)
• 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)
• Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective [12.490990352972695]
  We present a novel single-shot phase retrieval paradigm from a fractional Fourier transform perspective. The intensity measurement in the FrFT domain proves highly effective in alleviating the ambiguities of phase retrieval. The proposed self-supervised reconstruction approach harnesses the fast discrete algorithm of FrFT alongside untrained neural network priors.
  arXiv  Detail & Related papers  (2023-11-18T03:11:31Z)
• Efficient Frequency Domain-based Transformers for High-Quality Image Deblurring [39.720032882926176]
  We present an effective and efficient method that explores the properties of Transformers in the frequency domain for high-quality image deblurring. We formulate the proposed FSAS and DFFN into an asymmetrical network based on an encoder and decoder architecture.
  arXiv  Detail & Related papers  (2022-11-22T13:08:03Z)
• Deep Fourier Up-Sampling [100.59885545206744]
  Up-sampling in the Fourier domain is more challenging as it does not follow such a local property. We propose a theoretically sound Deep Fourier Up-Sampling (FourierUp) to solve these issues.
  arXiv  Detail & Related papers  (2022-10-11T06:17:31Z)
• PREF: Phasorial Embedding Fields for Compact Neural Representations [54.44527545923917]
  We present a phasorial embedding field emphPREF as a compact representation to facilitate neural signal modeling and reconstruction tasks. Our experiments show PREF-based neural signal processing technique is on par with the state-of-the-art in 2D image completion, 3D SDF surface regression, and 5D radiance field reconstruction.
  arXiv  Detail & Related papers  (2022-05-26T17:43:03Z)
• Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging [142.11622043078867]
  We propose a principled Degradation-Aware Unfolding Framework (DAUF) that estimates parameters from the compressed image and physical mask, and then uses these parameters to control each iteration. By plugging HST into DAUF, we establish the first Transformer-based deep unfolding method, Degradation-Aware Unfolding Half-Shuffle Transformer (DAUHST) for HSI reconstruction.
  arXiv  Detail & Related papers  (2022-05-20T11:37:44Z)
• Fourier Disentangled Space-Time Attention for Aerial Video Recognition [54.80846279175762]
  We present an algorithm, Fourier Activity Recognition (FAR), for UAV video activity recognition. Our formulation uses a novel Fourier object disentanglement method to innately separate out the human agent from the background. We have evaluated our approach on multiple UAV datasets including UAV Human RGB, UAV Human Night, Drone Action, and NEC Drone.
  arXiv  Detail & Related papers  (2022-03-21T01:24:53Z)
• Solving Phase Retrieval with a Learned Reference [18.76940558836028]
  Fourier phase retrieval is a classical problem that deals with the recovery of an image from the amplitude measurements of its Fourier coefficients. In this paper, we assume that a known (learned) reference is added to the signal before capturing the amplitude measurements. Our method is inspired by the principle of adding a reference signal in holography.
  arXiv  Detail & Related papers  (2020-07-29T06:17:25Z)

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.