TY  - CONF
AU  - Mohamed Ramzy Ibrahim
AU  - Robert Benavente
AU  - Daniel Ponsa
AU  - Felipe Lumbreras
PY  - 2024//
TI  - SWViT-RRDB: Shifted Window Vision Transformer Integrating Residual in Residual Dense Block for Remote Sensing Super-Resolution
BT  - 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications
N2  - Remote sensing applications, impacted by acquisition season and sensor variety, require high-resolution images. Transformer-based models improve satellite image super-resolution but are less effective than convolutional neural networks (CNNs) at extracting local details, crucial for image clarity. This paper introduces SWViT-RRDB, a new deep learning model for satellite imagery super-resolution. The SWViT-RRDB, combining transformer with convolution and attention blocks, overcomes the limitations of existing models by better representing small objects in satellite images. In this model, a pipeline of residual fusion group (RFG) blocks is used to combine the multi-headed self-attention (MSA) with residual in residual dense block (RRDB). This combines global and local image data for better super-resolution. Additionally, an overlapping cross-attention block (OCAB) is used to enhance fusion and allow interaction between neighboring pixels to maintain long-range pixel dependencies across the image. The SWViT-RRDB model and its larger variants outperform state-of-the-art (SoTA) models on two different satellite datasets in terms of PSNR and SSIM.
UR  - https://www.insticc.org/node/TechnicalProgram/visigrapp/2024/presentationDetails/123993
N1  - MSIAU
ID  - Mohamed Ramzy Ibrahim2024
ER  -