Enhancing satellite image quality with the edge-based wavelet transformer for super-resolution
Chieh Tsai, Pei‐Jun Lee, Shimaa Bergies, John C. Liobe, Vaidotas Barzdėnas · Applied Computing and Geosciences · 2025
High-quality satellite imagery is critical in environmental monitoring, disaster response, and urban planning applications, where detailed and accurate images are essential for informed decision-making. However, images from small satellites often have low resolution, limiting their effectiveness in addressing precise analysis challenges. To overcome these limitations, this paper presents the Edge-Based Wavelet Transformer for Super-Resolution (EBWT-SR), an innovative technique designed to enhance satellite image resolution while optimizing computational efficiency. EBWT-SR combines Spatial-Wavelet Multi-Head Attention Mechanisms and a Multi-Modal Convolutional Shallow Feature Extractor within a Convolutional Transformer architecture, allowing for the refinement of object contours and textures. By incorporating edge-based wavelet transform convolutional layers and a specialized multi-modal loss function for fine-tuning, the developed EBWT-SR improves local feature representation without increasing computational complexity. The new model can improve the results by approximately 0.67 in Peak Signal-to-Noise Ratio (PSNR) and 0.63 in Perceptually Uniform Peak Signal-to-Noise Ratio (puPSNR) metrics, along with a 7.7% reduction in Giga Floating-Point Operations Per Second (GFLOPS) compared to recent methods on the fine-grained satellite image dataset focused on ship classification and super-resolution tasks (FGCSR-42) dataset. highlighting its ability to enhance satellite image quality while significantly maintaining computational efficiency.