loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Authors: Mohamed Ibrahim 1 ; 2 ; Robert Benavente 2 ; Daniel Ponsa 2 and Felipe Lumbreras 2

Affiliations: 1 Computer Engineering Department, Arab Academy for Science, Technology and Maritime Transport, Alexandria, Egypt ; 2 Computer Vision Center & Computer Science Department, Universitat Autònoma de Barcelona, Barcelona, Spain

Keyword(s): Computer Vision, Super-Resolution, Remote Sensing, Deep Learning.

Abstract: 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. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 3.230.173.188

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Ibrahim, M.; Benavente, R.; Ponsa, D. and Lumbreras, F. (2024). SWViT-RRDB: Shifted Window Vision Transformer Integrating Residual in Residual Dense Block for Remote Sensing Super-Resolution. In Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP; ISBN 978-989-758-679-8; ISSN 2184-4321, SciTePress, pages 575-582. DOI: 10.5220/0012399300003660

@conference{visapp24,
author={Mohamed Ibrahim. and Robert Benavente. and Daniel Ponsa. and Felipe Lumbreras.},
title={SWViT-RRDB: Shifted Window Vision Transformer Integrating Residual in Residual Dense Block for Remote Sensing Super-Resolution},
booktitle={Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP},
year={2024},
pages={575-582},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012399300003660},
isbn={978-989-758-679-8},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 3: VISAPP
TI - SWViT-RRDB: Shifted Window Vision Transformer Integrating Residual in Residual Dense Block for Remote Sensing Super-Resolution
SN - 978-989-758-679-8
IS - 2184-4321
AU - Ibrahim, M.
AU - Benavente, R.
AU - Ponsa, D.
AU - Lumbreras, F.
PY - 2024
SP - 575
EP - 582
DO - 10.5220/0012399300003660
PB - SciTePress