Toward task-driven satellite image super-resolution

• URL: http://arxiv.org/abs/2503.15474v1
• Date: Wed, 19 Mar 2025 17:49:27 GMT
• Title: Toward task-driven satellite image super-resolution
• Authors: Maciej Ziaja, Pawel Kowaleczko, Daniel Kostrzewa, Nicolas Longépé, Michal Kawulok,
• Abstract summary: Super-resolution is aimed at reconstructing high-resolution images from low-resolution observations.<n>It often remains unclear whether the reconstructed details are close to the actual ground-truth information.<n>We present our efforts toward learning super-resolution algorithms in a task-driven way to make them suitable for generating high-resolution images.
• Score: 3.1457219084519004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Super-resolution is aimed at reconstructing high-resolution images from low-resolution observations. State-of-the-art approaches underpinned with deep learning allow for obtaining outstanding results, generating images of high perceptual quality. However, it often remains unclear whether the reconstructed details are close to the actual ground-truth information and whether they constitute a more valuable source for image analysis algorithms. In the reported work, we address the latter problem, and we present our efforts toward learning super-resolution algorithms in a task-driven way to make them suitable for generating high-resolution images that can be exploited for automated image analysis. In the reported initial research, we propose a methodological approach for assessing the existing models that perform computer vision tasks in terms of whether they can be used for evaluating super-resolution reconstruction algorithms, as well as training them in a task-driven way. We support our analysis with experimental study and we expect it to establish a solid foundation for selecting appropriate computer vision tasks that will advance the capabilities of real-world super-resolution.

Related papers

• Rewards-based image analysis in microscopy [2.906546126874626]
  Analyzing imaging and hyperspectral data is crucial across scientific fields, including biology, medicine, chemistry, and physics.<n>Currently, this task relies on complex, human-designed iterative steps such as denoising, spatial sampling, keypoint detection, feature generation, clustering, dimensionality reduction, and physics-based deconvolutions.<n>The introduction of machine learning over the past decade has accelerated tasks like image segmentation and object detection via supervised learning, and dimensionality reduction via unsupervised methods.<n>Here, we discuss advances in reward-based, which adopt expert decision-making principles and demonstrate strong transfer learning across
  arXiv  Detail & Related papers  (2025-02-23T19:19:38Z)
• Task-driven single-image super-resolution reconstruction of document scans [2.8391355909797644]
  We investigate the possibility of employing super-resolution as a preprocessing step to improve optical character recognition from document scans.<n>To achieve that, we propose to train deep networks for single-image super-resolution in a task-driven way to make them better adapted for the purpose of text detection.
  arXiv  Detail & Related papers  (2024-07-12T05:18:26Z)
• Reconstructing Interpretable Features in Computational Super-Resolution microscopy via Regularized Latent Search [2.7194314957925094]
  Supervised deep learning approaches can artificially increase the resolution of microscopy images by learning a mapping between two image resolutions or modalities. Recent methods based on GAN latent search offered a drastic increase in resolution without the need of paired images. Here, we propose a robust super-resolution method based on regularized latent search(RLS) that offers an actionable balance between fidelity to the ground-truth and realism of the recovered image.
  arXiv  Detail & Related papers  (2024-05-29T14:20:46Z)
• High-Resolution Vision Transformers for Pixel-Level Identification of Structural Components and Damage [1.8923948104852863]
  We develop a semantic segmentation network based on vision transformers and Laplacian pyramids scaling networks. The proposed framework has been evaluated through comprehensive experiments on a dataset of bridge inspection report images.
  arXiv  Detail & Related papers  (2023-08-06T03:34:25Z)
• Super-Resolving Face Image by Facial Parsing Information [52.1267613768555]
  Face super-resolution is a technology that transforms a low-resolution face image into the corresponding high-resolution one. We build a novel parsing map guided face super-resolution network which extracts the face prior from low-resolution face image. High-resolution features contain more precise spatial information while low-resolution features provide strong contextual information.
  arXiv  Detail & Related papers  (2023-04-06T08:19:03Z)
• Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection [77.3530907443279]
  We propose a novel self-supervised framework to detect objects in degraded low resolution images. Our methods has achieved superior performance compared with existing methods when facing variant degradation situations.
  arXiv  Detail & Related papers  (2022-08-05T09:36:13Z)
• 3D Human Pose, Shape and Texture from Low-Resolution Images and Videos [107.36352212367179]
  We propose RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. The proposed method is able to learn 3D body pose and shape across different resolutions with one single model. We extend the RSC-Net to handle low-resolution videos and apply it to reconstruct textured 3D pedestrians from low-resolution input.
  arXiv  Detail & Related papers  (2021-03-11T06:52:12Z)
• Analysis and evaluation of Deep Learning based Super-Resolution algorithms to improve performance in Low-Resolution Face Recognition [0.0]
  Super-resolution algorithms may be able to recover the discriminant properties of the subjects involved. This project aimed at evaluating and adapting different deep neural network architectures for the task of face super-resolution. Experiments showed that general super-resolution architectures might enhance face verification performance of deep neural networks trained on high-resolution faces.
  arXiv  Detail & Related papers  (2021-01-19T02:41:57Z)
• 3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning [105.49950571267715]
  Existing deep learning methods for 3D human shape and pose estimation rely on relatively high-resolution input images. We propose RSC-Net, which consists of a Resolution-aware network, a Self-supervision loss, and a Contrastive learning scheme. We show that both these new training losses provide robustness when learning 3D shape and pose in a weakly-supervised manner.
  arXiv  Detail & Related papers  (2020-07-27T16:19:52Z)
• Efficient Poverty Mapping using Deep Reinforcement Learning [75.6332944247741]
  High-resolution satellite imagery and machine learning have proven useful in many sustainability-related tasks. The accuracy afforded by high-resolution imagery comes at a cost, as such imagery is extremely expensive to purchase at scale. We propose a reinforcement learning approach in which free low-resolution imagery is used to dynamically identify where to acquire costly high-resolution images.
  arXiv  Detail & Related papers  (2020-06-07T18:30:57Z)
• 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.