D$^{2}$R$^{2}$OSR: Degradation-Disentangled Representation for Real-World Omnidirectional Image Super-Resolution

• URL: http://arxiv.org/abs/2606.29314v1
• Date: Sun, 28 Jun 2026 10:21:26 GMT
• Title: D$^{2}$R$^{2}$OSR: Degradation-Disentangled Representation for Real-World Omnidirectional Image Super-Resolution
• Abstract summary: D$2$R$2$OSR is a Degradation-Disentangled Representation framework for Real-world Omnidirectional image Super-Resolution.<n>D$2$R$2$OSR achieves state-of-the-art performance and produces visually compelling, high-fidelity omnidirectional Real-SR results.
• Score: 26.967075259566553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the growing demand for immersive visual experiences, high-quality omnidirectional images (ODIs) have become increasingly important. However, limitations in imaging devices and transmission bandwidth often lead to low-resolution ODIs, hindering the rendering of fine-grained 360° details, especially in the presence of real-world degradations and geometric distortions. Existing real-world super-resolution (Real-SR) methods are inadequate for ODIs, as their degradation models fail to account for the complex imaging pipeline involving fisheye capture and Equirectangular Projection (ERP), introducing severe aliasing and projection-specific distortions. To address these challenges, we propose D$^{2}$R$^{2}$OSR, a Degradation-Disentangled Representation framework for Real-world Omnidirectional image Super-Resolution. D$^{2}$R$^{2}$OSR explicitly models degradations arising from both fisheye imaging and ERP projection, guided by two key insights: (1) projection priors play a critical role in shaping real-world degradations, and (2) human perception in immersive environments is inherently viewpoint-centric. Accordingly, we introduce a Perspective Projection Representation (PPR) operating alongside the ERP branch to capture viewpoint-aware features, together with a Degradation-Specific Module (DSM) that jointly models ERP-induced geometric distortions and PPR-specific real-world degradations. Extensive experiments demonstrate that D$^{2}$R$^{2}$OSR achieves state-of-the-art performance and produces visually compelling, high-fidelity omnidirectional Real-SR results while maintaining favorable computational efficiency for low-resource deployment.

Related papers

• Multi-level distortion-aware deformable network for omnidirectional image super-resolution [25.937762776025718]
  We propose a novel Multi-level Distortion-aware Deformable Network (MDDN) for OmniDirectional Images (ODIs)<n>This architecture expands the sampling range to include more distorted patterns across wider areas.<n>Experiments on publicly available datasets demonstrate that MDDN outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2025-12-19T08:35:08Z)
• Dual-domain Adaptation Networks for Realistic Image Super-resolution [81.34345637776408]
  Realistic image super-resolution (SR) focuses on transforming real-world low-resolution (LR) images into high-resolution (HR) ones.<n>Current methods struggle with limited real-world LR-HR data, impacting the learning of basic image features.<n>We introduce a novel approach, which is able to efficiently adapt pre-trained image SR models from simulated to real-world datasets.
  arXiv  Detail & Related papers  (2025-11-21T12:57:23Z)
• Unsupervised Real-World Super-Resolution via Rectified Flow Degradation Modelling [16.485914675883972]
  Unsupervised real-world super-resolution (SR) faces critical challenges due to the complex, unknown degradation distributions.<n>Existing methods struggle to generalize from synthetic low-resolution (LR) and high-resolution (HR) image pairs to real-world data.<n>We propose an unsupervised real-world SR method based on rectified flow to effectively capture and model real-world degradation.
  arXiv  Detail & Related papers  (2025-08-10T07:27:28Z)
• Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns [4.977925450373957]
  We propose a novel TripleGAN framework for training super-resolution reconstruction models.<n>The framework learns real-world degradation patterns from LR observations and synthesizes datasets with corresponding degradation characteristics.<n>Our method exhibits clear advantages in quantitative metrics while maintaining sharp reconstructions without over-smoothing artifacts.
  arXiv  Detail & Related papers  (2025-06-20T14:24:48Z)
• Towards Realistic Data Generation for Real-World Super-Resolution [58.99206459754721]
  RealDGen is an unsupervised learning data generation framework designed for real-world super-resolution.<n>We develop content and degradation extraction strategies, which are integrated into a novel content-degradation decoupled diffusion model.<n>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)
• GEOcc: Geometrically Enhanced 3D Occupancy Network with Implicit-Explicit Depth Fusion and Contextual Self-Supervision [49.839374549646884]
  This paper presents GEOcc, a Geometric-Enhanced Occupancy network tailored for vision-only surround-view perception.<n>Our approach achieves State-Of-The-Art performance on the Occ3D-nuScenes dataset with the least image resolution needed and the most weightless image backbone.
  arXiv  Detail & Related papers  (2024-05-17T07:31:20Z)
• Learning Dual-Level Deformable Implicit Representation for Real-World Scale Arbitrary Super-Resolution [81.74583887661794]
  We build a new real-world super-resolution benchmark with both integer and non-integer scaling factors. We propose a Dual-level Deformable Implicit Representation (DDIR) to solve real-world scale arbitrary super-resolution. Our trained model achieves state-of-the-art performance on the RealArbiSR and RealSR benchmarks for real-world scale arbitrary super-resolution.
  arXiv  Detail & Related papers  (2024-03-16T13:44:42Z)
• DR2: Diffusion-based Robust Degradation Remover for Blind Face Restoration [66.01846902242355]
  Blind face restoration usually synthesizes degraded low-quality data with a pre-defined degradation model for training. It is expensive and infeasible to include every type of degradation to cover real-world cases in the training data. We propose Robust Degradation Remover (DR2) to first transform the degraded image to a coarse but degradation-invariant prediction, then employ an enhancement module to restore the coarse prediction to a high-quality image.
  arXiv  Detail & Related papers  (2023-03-13T06:05:18Z)
• Real-World Image Super Resolution via Unsupervised Bi-directional Cycle Domain Transfer Learning based Generative Adversarial Network [14.898170534545727]
  We propose the Unsupervised Bi-directional Cycle Domain Transfer Learning-based Generative Adrial Network (UBCDTLGAN) First, the UBCDTN is able to produce an approximated real-like LR image through transferring the LR image from an artificially degraded domain to the real-world image domain. Second, the SESRN has the ability to super-resolve the approximated real-like LR image to a photo-realistic HR image.
  arXiv  Detail & Related papers  (2022-11-19T02:19:21Z)
• HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging [138.04956118993934]
  We propose a high-resolution dual-domain learning network (HDNet) for HSI reconstruction. On the one hand, the proposed HR spatial-spectral attention module with its efficient feature fusion provides continuous and fine pixel-level features. On the other hand, frequency domain learning (FDL) is introduced for HSI reconstruction to narrow the frequency domain discrepancy.
  arXiv  Detail & Related papers  (2022-03-04T06:37:45Z)
• Joint Generative Learning and Super-Resolution For Real-World Camera-Screen Degradation [6.14297871633911]
  In real-world single image super-resolution (SISR) task, the low-resolution image suffers more complicated degradations. In this paper, we focus on the camera-screen degradation and build a real-world dataset (Cam-ScreenSR) We propose a joint two-stage model. Firstly, the downsampling degradation GAN(DD-GAN) is trained to model the degradation and produces more various of LR images. Then the dual residual channel attention network (DuRCAN) learns to recover the SR image.
  arXiv  Detail & Related papers  (2020-08-01T07:10:13Z)

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.