LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)
Abstract
Recent advances in image-to-image translation involve the integration of non-visual imagery in deep models. Non-visual sensors, although more costly, often produce low-resolution images. To combat this, methods using RGB images to enhance the resolution of these modalities have been introduced. Fusing these modalities to achieve high-resolution results demands models with millions of parameters and extended inference times. We present LaMAR, a lightweight model. It employs Laplacian image pyramids combined with a low-resolution thermal image for Guided Thermal Super Resolution. By decomposing the RGB image into a Laplacian pyramid, LaMAR preserves image details and avoids high-resolution feature map computations, ensuring efficiency. With faster inference times and fewer parameters, our model demonstrates state-of-the-art results.
Cite
Text
Kasliwal et al. "LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024. doi:10.1609/AAAI.V38I21.30463Markdown
[Kasliwal et al. "LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)." AAAI Conference on Artificial Intelligence, 2024.](https://mlanthology.org/aaai/2024/kasliwal2024aaai-lamar/) doi:10.1609/AAAI.V38I21.30463BibTeX
@inproceedings{kasliwal2024aaai-lamar,
title = {{LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution (Student Abstract)}},
author = {Kasliwal, Aditya and Kamani, Aryan and Gakhar, Ishaan and Seth, Pratinav and Rallabandi, Sriya},
booktitle = {AAAI Conference on Artificial Intelligence},
year = {2024},
pages = {23539-23541},
doi = {10.1609/AAAI.V38I21.30463},
url = {https://mlanthology.org/aaai/2024/kasliwal2024aaai-lamar/}
}