Solar multi-object multi-frame blind deconvolution with a spatially variant convolution neural emulator

• URL: http://arxiv.org/abs/2405.09864v1
• Date: Thu, 16 May 2024 07:42:39 GMT
• Title: Solar multi-object multi-frame blind deconvolution with a spatially variant convolution neural emulator
• Authors: A. Asensio Ramos,
• Abstract summary: This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions. By training on a dataset of images convolved with spatially invariant point spread functions, this approach presents a significant advancement over traditional methods.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The study of astronomical phenomena through ground-based observations is always challenged by the distorting effects of Earth's atmosphere. Traditional methods of post-facto image correction, essential for correcting these distortions, often rely on simplifying assumptions that limit their effectiveness, particularly in the presence of spatially variant atmospheric turbulence. Such cases are often solved by partitioning the field-of-view into small patches, deconvolving each patch independently, and merging all patches together. This approach is often inefficient and can produce artifacts. Recent advancements in computational techniques and the advent of deep learning offer new pathways to address these limitations. This paper introduces a novel framework leveraging a deep neural network to emulate spatially variant convolutions, offering a breakthrough in the efficiency and accuracy of astronomical image deconvolution. By training on a dataset of images convolved with spatially invariant point spread functions and validating its generalizability to spatially variant conditions, this approach presents a significant advancement over traditional methods. The convolution emulator is used as a forward model in a multi-object multi-frame blind deconvolution algorithm for solar images. The emulator enables the deconvolution of solar observations across large fields of view without resorting to patch-wise mosaicking, thus avoiding artifacts associated with such techniques. This method represents a significant computational advantage, reducing processing times by orders of magnitude.

Related papers

• STLight: a Fully Convolutional Approach for Efficient Predictive Learning by Spatio-Temporal joint Processing [6.872340834265972]
  We propose STLight, a novel method for S-temporal learning that relies solely on channel-wise and depth-wise convolutions as learnable layers. STLight overcomes the limitations of traditional convolutional approaches by rearranging spatial and temporal dimensions together. Our architecture achieves state-of-the-art performance on STL benchmarks across datasets and settings, while significantly improving computational efficiency in terms of parameters and computational FLOPs.
  arXiv  Detail & Related papers  (2024-11-15T13:53:19Z)
• Blind Time-of-Flight Imaging: Sparse Deconvolution on the Continuum with Unknown Kernels [8.59982222642104]
  computational Time-of-Flight (ToF) imaging has emerged as an exciting and a novel imaging modality. This paper introduces a novel, blind ToF imaging technique that does not require kernel calibration and recovers sparse spikes on a continuum.
  arXiv  Detail & Related papers  (2024-10-31T22:42:02Z)
• Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration [64.84134880709625]
  We show that it is possible to perform domain adaptation via the noise space using diffusion models. In particular, by leveraging the unique property of how auxiliary conditional inputs influence the multi-step denoising process, we derive a meaningful diffusion loss. We present crucial strategies such as channel-shuffling layer and residual-swapping contrastive learning in the diffusion model.
  arXiv  Detail & Related papers  (2024-06-26T17:40:30Z)
• Accelerating Multiframe Blind Deconvolution via Deep Learning [0.0]
  Ground-based solar image restoration is a computationally expensive procedure. We propose a new method to accelerate the restoration based on algorithm unrolling. We show that both methods significantly reduce the restoration time compared to the standard optimization procedure.
  arXiv  Detail & Related papers  (2023-06-21T07:53:00Z)
• RISP: Rendering-Invariant State Predictor with Differentiable Simulation and Rendering for Cross-Domain Parameter Estimation [110.4255414234771]
  Existing solutions require massive training data or lack generalizability to unknown rendering configurations. We propose a novel approach that marries domain randomization and differentiable rendering gradients to address this problem. Our approach achieves significantly lower reconstruction errors and has better generalizability among unknown rendering configurations.
  arXiv  Detail & Related papers  (2022-05-11T17:59:51Z)
• Coarse-to-Fine Sparse Transformer for Hyperspectral Image Reconstruction [138.04956118993934]
  We propose a novel Transformer-based method, coarse-to-fine sparse Transformer (CST) CST embedding HSI sparsity into deep learning for HSI reconstruction. In particular, CST uses our proposed spectra-aware screening mechanism (SASM) for coarse patch selecting. Then the selected patches are fed into our customized spectra-aggregation hashing multi-head self-attention (SAH-MSA) for fine pixel clustering and self-similarity capturing.
  arXiv  Detail & Related papers  (2022-03-09T16:17:47Z)
• Learning Discriminative Shrinkage Deep Networks for Image Deconvolution [122.79108159874426]
  We propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy.
  arXiv  Detail & Related papers  (2021-11-27T12:12:57Z)
• Video Frame Interpolation Transformer [86.20646863821908]
  We propose a Transformer-based video framework that allows content-aware aggregation weights and considers long-range dependencies with the self-attention operations. To avoid the high computational cost of global self-attention, we introduce the concept of local attention into video. In addition, we develop a multi-scale frame scheme to fully realize the potential of Transformers.
  arXiv  Detail & Related papers  (2021-11-27T05:35:10Z)
• Manifold-Inspired Single Image Interpolation [17.304301226838614]
  Many approaches to single image use manifold models to exploit semi-local similarity. aliasing in the input image makes it challenging for both parts. We propose a carefully-designed adaptive technique to remove aliasing in severely aliased regions. This technique enables reliable identification of similar patches even in the presence of strong aliasing.
  arXiv  Detail & Related papers  (2021-07-31T04:29:05Z)
• Learning to do multiframe wavefront sensing unsupervisedly: applications to blind deconvolution [0.0]
  We propose an unsupervised training scheme for blind deconvolution deep learning systems. It can be applied for the correction of point-like as well as extended objects. The network model is roughly three orders of magnitude faster than applying standard deconvolution.
  arXiv  Detail & Related papers  (2020-06-02T08:02:12Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.