Imaging with super-resolution in changing random media

• URL: http://arxiv.org/abs/2511.14147v1
• Date: Tue, 18 Nov 2025 05:18:00 GMT
• Title: Imaging with super-resolution in changing random media
• Authors: Alexander Christie, Matan Leibovich, Miguel Moscoso, Alexei Novikov, George Papanicolaou, Chrysoula Tsogka,
• Abstract summary: We develop an imaging algorithm that exploits strong scattering to achieve super-resolution in changing random media.<n>The method processes large and diverse datasets using sparse dictionary learning, clustering, and multidimensional scaling.
• Score: 36.517459527015326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We develop an imaging algorithm that exploits strong scattering to achieve super-resolution in changing random media. The method processes large and diverse array datasets using sparse dictionary learning, clustering, and multidimensional scaling. Starting from random initializations, the algorithm reliably extracts the unknown medium properties necessary for accurate imaging using back-propagation, $\ell_2$ or $\ell_1$ methods. Remarkably, scattering enhances resolution beyond homogeneous medium limits. When abundant data are available, the algorithm allows the realization of super-resolution in imaging.

Related papers

• Fast Isotropic Median Filtering [0.0]
  Median filtering is a cornerstone of computational image processing.<n>Our method operates efficiently on arbitrary bit-depth data, arbitrary kernel sizes, and arbitrary convex kernel shapes.
  arXiv  Detail & Related papers  (2025-05-28T23:38:21Z)
• High-Precision Dichotomous Image Segmentation via Probing Diffusion Capacity [69.32473738284374]
  Diffusion models have revolutionized text-to-image synthesis by delivering exceptional quality, fine detail resolution, and strong contextual awareness.<n>We propose DiffDIS, a diffusion-driven segmentation model that taps into the potential of the pre-trained U-Net within diffusion models.<n>Experiments on the DIS5K dataset demonstrate the superiority of DiffDIS, achieving state-of-the-art results through a streamlined inference process.
  arXiv  Detail & Related papers  (2024-10-14T02:49:23Z)
• Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-Resolution [6.055006354743854]
  We develop an algorithm that utilize a low-resolution image combined with features extracted from multiple low-quality images to generate a super-resolved image. Unlike other algorithms, our approach recovers facial features without explicitly providing attribute information. This is the first time multi-features combined with low-resolution images are used as conditioners to generate more reliable super-resolution images.
  arXiv  Detail & Related papers  (2024-08-27T20:08:33Z)
• Blind Image Deblurring with FFT-ReLU Sparsity Prior [1.179778723980276]
  Blind image deblurring is the process of recovering a sharp image from a blurred one without prior knowledge about the blur kernel. We introduce a method that leverages a prior which targets the blur kernel to achieve effective deblurring across a wide range of image types.
  arXiv  Detail & Related papers  (2024-06-12T15:51:39Z)
• Learning from small data sets: Patch-based regularizers in inverse problems for image reconstruction [1.1650821883155187]
  Recent advances in machine learning require a huge amount of data and computer capacity to train the networks. Our paper addresses the issue of learning from small data sets by taking patches of very few images into account. We show how we can achieve uncertainty quantification by approximating the posterior using Langevin Monte Carlo methods.
  arXiv  Detail & Related papers  (2023-12-27T15:30:05Z)
• DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion [144.9653045465908]
  We propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM) Our approach yields promising fusion results in infrared-visible image fusion and medical image fusion.
  arXiv  Detail & Related papers  (2023-03-13T04:06:42Z)
• Deep Model-Based Super-Resolution with Non-uniform Blur [1.7188280334580197]
  We propose a state-of-the-art method for super-resolution with non-uniform blur. We first propose a fast deep plug-and-play algorithm, based on linearized ADMM splitting techniques. We unfold our iterative algorithm into a single network and train it end-to-end.
  arXiv  Detail & Related papers  (2022-04-21T13:57:21Z)
• Learning Enriched Features for Fast Image Restoration and Enhancement [166.17296369600774]
  This paper presents a holistic goal of maintaining spatially-precise high-resolution representations through the entire network. We learn an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details. Our approach achieves state-of-the-art results for a variety of image processing tasks, including defocus deblurring, image denoising, super-resolution, and image enhancement.
  arXiv  Detail & Related papers  (2022-04-19T17:59:45Z)
• PixelPyramids: Exact Inference Models from Lossless Image Pyramids [58.949070311990916]
  Pixel-Pyramids is a block-autoregressive approach with scale-specific representations to encode the joint distribution of image pixels. It yields state-of-the-art results for density estimation on various image datasets, especially for high-resolution data. For CelebA-HQ 1024 x 1024, we observe that the density estimates are improved to 44% of the baseline despite sampling speeds superior even to easily parallelizable flow-based models.
  arXiv  Detail & Related papers  (2021-10-17T10:47:29Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

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.