Lightweight omnidirectional super-resolution via frequency-spatial fusion and equirectangular projection correction

Dezhi Li, Yonglin Chen, Xingbo Dong, Tiong-Sik Ng, Zhe Jin, Shengyuan Wang, Wen Sha

Abstract: Omnidirectional images (ODIs) possess unique equirectangular projection geometric properties that pose challenges for traditional super-resolution methods. Existing ODI super-resolution (ODISR) models often struggle to effectively capture both spatial and frequency domain information, limiting their performance. We introduce a lightweight model that fuses information from both domains to enhance ODISR. A dual-domain attention mechanism tailored for ODISR is proposed, incorporating a reparameterized pixel attention module and a frequency-domain attention module. This approach achieves a balance between efficiency and reconstruction quality, reducing both time and space complexity. Experiments demonstrate that our model outperforms state-of-the-art lightweight ODISR models while maintaining competitive performance.