Omnidirectional image super-resolution via position attention network.
Journal:
Neural networks : the official journal of the International Neural Network Society
Published Date:
Jun 15, 2024
Abstract
For convenient transmission, omnidirectional images (ODIs) usually follow the equirectangular projection (ERP) format and are low-resolution. To provide better immersive experience, omnidirectional image super resolution (ODISR) is essential. However, ERP ODIs suffer from serious geometric distortion and pixel stretching across latitudes, generating massive redundant information at high latitudes. This characteristic poses a huge challenge for the traditional SR methods, which can only obtain the suboptimal ODISR performance. To address this issue, we propose a novel position attention network (PAN) for ODISR in this paper. Specifically, a two-branch structure is introduced, in which the basic enhancement branch (BE) serves to achieve coarse deep feature enhancement for extracted shallow features. Meanwhile, the position attention enhancement branch (PAE) builds a positional attention mechanism to dynamically adjust the contribution of features at different latitudes in the ERP representation according to their positions and stretching degrees, which achieves the enhancement for the differentiated information, suppresses the redundant information, and modulate the deep features with spatial distortion. Subsequently, the features of two branches are fused effectively to achieve the further refinement and adapt the distortion characteristic of ODIs. After that, we exploit a long-term memory module (LM), promoting information interactions and fusions between the branches to enhance the perception of the distortion, aggregating the prior hierarchical features to keep the long-term memory and boosting the ODISR performance. Extensive results demonstrate the state-of-the-art performance and the high efficiency of our PAN in ODISR.