Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network

• URL: http://arxiv.org/abs/2404.03930v1
• Date: Fri, 5 Apr 2024 07:24:10 GMT
• Title: Real-GDSR: Real-World Guided DSM Super-Resolution via Edge-Enhancing Residual Network
• Authors: Daniel Panangian, Ksenia Bittner,
• Abstract summary: A low-resolution digital surface model (DSM) features distinctive attributes impacted by noise, sensor limitations and data acquisition conditions. This causes super-resolution models trained on synthetic data does not perform effectively on real ones. We introduce a novel methodology to address the intricacies of real-world DSM super-resolution, named REAL-GDSR.
• Score: 2.3020018305241337
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A low-resolution digital surface model (DSM) features distinctive attributes impacted by noise, sensor limitations and data acquisition conditions, which failed to be replicated using simple interpolation methods like bicubic. This causes super-resolution models trained on synthetic data does not perform effectively on real ones. Training a model on real low and high resolution DSMs pairs is also a challenge because of the lack of information. On the other hand, the existence of other imaging modalities of the same scene can be used to enrich the information needed for large-scale super-resolution. In this work, we introduce a novel methodology to address the intricacies of real-world DSM super-resolution, named REAL-GDSR, breaking down this ill-posed problem into two steps. The first step involves the utilization of a residual local refinement network. This strategic approach departs from conventional methods that trained to directly predict height values instead of the differences (residuals) and utilize large receptive fields in their networks. The second step introduces a diffusion-based technique that enhances the results on a global scale, with a primary focus on smoothing and edge preservation. Our experiments underscore the effectiveness of the proposed method. We conduct a comprehensive evaluation, comparing it to recent state-of-the-art techniques in the domain of real-world DSM super-resolution (SR). Our approach consistently outperforms these existing methods, as evidenced through qualitative and quantitative assessments.

Related papers

• TDDSR: Single-Step Diffusion with Two Discriminators for Super Resolution [28.174638880324014]
  We propose TDDSR, an efficient single-step diffusion-based super-resolution method. Our method, distilled from a pre-trained teacher model and based on a diffusion network, performs super-resolution in a single step. Experimental results demonstrate its effectiveness across real-world and face-specific SR tasks.
  arXiv  Detail & Related papers  (2024-10-10T07:12:46Z)
• Degradation-Guided One-Step Image Super-Resolution with Diffusion Priors [75.24313405671433]
  Diffusion-based image super-resolution (SR) methods have achieved remarkable success by leveraging large pre-trained text-to-image diffusion models as priors. We introduce a novel one-step SR model, which significantly addresses the efficiency issue of diffusion-based SR methods. Unlike existing fine-tuning strategies, we designed a degradation-guided Low-Rank Adaptation (LoRA) module specifically for SR.
  arXiv  Detail & Related papers  (2024-09-25T16:15:21Z)
• Towards Realistic Data Generation for Real-World Super-Resolution [58.88039242455039]
  RealDGen is an unsupervised learning data generation framework designed for real-world super-resolution. We develop content and degradation extraction strategies, which are integrated into a novel content-degradation decoupled diffusion model. Experiments demonstrate that RealDGen excels in generating large-scale, high-quality paired data that mirrors real-world degradations.
  arXiv  Detail & Related papers  (2024-06-11T13:34:57Z)
• Teacher-Student Network for Real-World Face Super-Resolution with Progressive Embedding of Edge Information [2.280954956645056]
  A real-world face super-resolution teacher-student model is proposed, which considers the domain gap between real and synthetic data. Our proposed approach surpasses state-of-the-art methods in obtaining high-quality face images for real-world FSR.
  arXiv  Detail & Related papers  (2024-05-08T02:48:52Z)
• Digital Twin-Enhanced Deep Reinforcement Learning for Resource Management in Networks Slicing [46.65030115953947]
  We propose a framework consisting of a digital twin and reinforcement learning agents. Specifically, we propose to use the historical data and the neural networks to build a digital twin model to simulate the state variation law of the real environment. We also extend the framework to offline reinforcement learning, where solutions can be used to obtain intelligent decisions based solely on historical data.
  arXiv  Detail & Related papers  (2023-11-28T15:25:14Z)
• Learning from Multi-Perception Features for Real-Word Image Super-resolution [87.71135803794519]
  We propose a novel SR method called MPF-Net that leverages multiple perceptual features of input images. Our method incorporates a Multi-Perception Feature Extraction (MPFE) module to extract diverse perceptual information. We also introduce a contrastive regularization term (CR) that improves the model's learning capability.
  arXiv  Detail & Related papers  (2023-05-26T07:35:49Z)
• Generalized Real-World Super-Resolution through Adversarial Robustness [107.02188934602802]
  We present Robust Super-Resolution, a method that leverages the generalization capability of adversarial attacks to tackle real-world SR. Our novel framework poses a paradigm shift in the development of real-world SR methods. By using a single robust model, we outperform state-of-the-art specialized methods on real-world benchmarks.
  arXiv  Detail & Related papers  (2021-08-25T22:43:20Z)
• Towards Accurate Knowledge Transfer via Target-awareness Representation Disentanglement [56.40587594647692]
  We propose a novel transfer learning algorithm, introducing the idea of Target-awareness REpresentation Disentanglement (TRED) TRED disentangles the relevant knowledge with respect to the target task from the original source model and used as a regularizer during fine-tuning the target model. Experiments on various real world datasets show that our method stably improves the standard fine-tuning by more than 2% in average.
  arXiv  Detail & Related papers  (2020-10-16T17:45:08Z)
• An Online Method for A Class of Distributionally Robust Optimization with Non-Convex Objectives [54.29001037565384]
  We propose a practical online method for solving a class of online distributionally robust optimization (DRO) problems. Our studies demonstrate important applications in machine learning for improving the robustness of networks.
  arXiv  Detail & Related papers  (2020-06-17T20:19:25Z)

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.