LaMAR: Laplacian Pyramid for Multimodal Adaptive Super Resolution

Document Type

Conference Proceeding

Publication Title

Proceedings of the AAAI Conference on Artificial Intelligence

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.

First Page

23539

Last Page

23541

DOI

10.1609/aaai.v38i21.30463

Publication Date

3-25-2024

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