Multi-level distortion-aware deformable network for omnidirectional image super-resolution

• URL: http://arxiv.org/abs/2512.17343v1
• Date: Fri, 19 Dec 2025 08:35:08 GMT
• Title: Multi-level distortion-aware deformable network for omnidirectional image super-resolution
• Authors: Cuixin Yang, Rongkang Dong, Kin-Man Lam, Yuhang Zhang, Guoping Qiu,
• Abstract summary: 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.
• Score: 25.937762776025718
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As augmented reality and virtual reality applications gain popularity, image processing for OmniDirectional Images (ODIs) has attracted increasing attention. OmniDirectional Image Super-Resolution (ODISR) is a promising technique for enhancing the visual quality of ODIs. Before performing super-resolution, ODIs are typically projected from a spherical surface onto a plane using EquiRectangular Projection (ERP). This projection introduces latitude-dependent geometric distortion in ERP images: distortion is minimal near the equator but becomes severe toward the poles, where image content is stretched across a wider area. However, existing ODISR methods have limited sampling ranges and feature extraction capabilities, which hinder their ability to capture distorted patterns over large areas. To address this issue, we propose a novel Multi-level Distortion-aware Deformable Network (MDDN) for ODISR, designed to expand the sampling range and receptive field. Specifically, the feature extractor in MDDN comprises three parallel branches: a deformable attention mechanism (serving as the dilation=1 path) and two dilated deformable convolutions with dilation rates of 2 and 3. This architecture expands the sampling range to include more distorted patterns across wider areas, generating dense and comprehensive features that effectively capture geometric distortions in ERP images. The representations extracted from these deformable feature extractors are adaptively fused in a multi-level feature fusion module. Furthermore, to reduce computational cost, a low-rank decomposition strategy is applied to dilated deformable convolutions. Extensive experiments on publicly available datasets demonstrate that MDDN outperforms state-of-the-art methods, underscoring its effectiveness and superiority in ODISR.

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