Enhancing satellite image quality with the edge-based wavelet transformer for super-resolution
Year: 2025
Type: article
Abstract: A B S T R A C THigh-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 face low resolution, limiting their effectiveness for 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 PSNR and 0.63 in puPSNR metrics, along with a 7.7% reduction in GFLOPS compared to recent methods on the FGCSR-42 dataset. highlighting its ability to enhance satellite image quality while significantly maintaining computational efficiency.
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,040
Awards: G1606696858, G4244901718, G4435955081
OpenAlex ID: W4415614725