Remote sensing image Super-resolution reconstruction by fusing multi-scale receptive fields and hybrid transformer.

ABSTRACT: To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resol

SUBMITTER: Liu D 

PROVIDER: S-EPMC11742027 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature