Efficient Adaptation for Real-World Omnidirectional Image Super-Resolution

With the increasing popularity of virtual techniques, such as virtual reality (VR) and augmented reality (AR), super-resolution (SR) of omnidirectional images has been crucial for more immersive and realistic experiences. This advancement also enhances the quality of images for various visual applications. Researchers have started exploring omnidirectional image super-resolution (ODISR). However, existing methods primarily address the problem using synthetic data pairs, where low-resolution (LR) images are generated using fixed, predefined kernels, such as bicubic downsampling. Consequently, the performance of these methods drops significantly when applied to real-world data. To address this issue, in this paper, we propose exploring the rich image priors from existing SR models designed for 2D planar images and adapting them for real-world ODISR. Specifically, we employ low-rank adaptation (LoRA) to adapt a large-scale model from the 2D planar image domain to the omnidirectional image domain by training only the decomposed matrices. This approach significantly reduces the number of parameters and computational resources required. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods both quantitatively and qualitatively.