SVIFNN: Robust Inpainting Fourier Neural Network for SST Scientific Visualization Image Leveraging Significant Stability and Nonsignificant Anomalies
Zijie Zuo, Jie Nie, Xin Wang, Junyu Dong · IEEE Transactions on Geoscience and Remote Sensing · 2025
The Scientific Visualization Images (SVI) of Sea Surface Temperature (SST) play a pivotal role as visual resources for investigating oceanographic processes. However, they are often plagued by extensive data gaps due to objective factors like cloud cover. Additionally, their content deviates significantly from ordinary images, posing challenges for conventional completion techniques. Given the intricate nature of marine systems, completing the visualization of sea surface temperature presents several challenges. Firstly, predicting missing segments relies not only on prominent patterns but also on subtle anomalies, which are often overlooked by methods focused on extracting prominent features. Secondly, these images exhibit chaos and lack clear semantics, making it difficult for methods primarily focused on semantic extraction to effectively complete them. To address these challenges, this study presents a novel method named the Inpainting Fourier Neural Network for SVI (SVIFNN). This approach employs a twin-stream architecture to highlight both significant stability and non-significant anomalies. Notably, it incorporates a "reverse attention mechanism" in the non-significant anomalies extraction stream to preserve unconventional information. Furthermore, by cascading Fourier neural operator (FNO), it leverages frequency domain characteristics to mitigate spatial chaos. Through a frequency domain feature extraction module, it achieves an adaptive fusion of significant stability and nonsignificant anomalies. Experiment results demonstrate SVIFNN’s superiority over State-Of-The-Art (SOTA) methods, particularly under a 68% missing rate condition. Significant improvements are observed inR2(18.1%, 19.3%, and 21.8%) and reductions inRMSE(22.6%, 28.8%, and 23.8%) across different Noise-to-Signal (N/S) ratios of 0.1, 0.2, and 0.3, respectively, underscoring SVIFNN’s robustness in handling SVIs extensive data gaps. Adequate ablation experiments further validate the effectiveness of the proposed non-significant anomalies extraction stream and frequency domain operators, with the latter demonstrating superior performance for scientific visualization images compared to traditional spatial domain CNN and ViT operators.