Ship in Sight: Diffusion Models for Ship-Image Super Resolution
Luigi Sigillo Riccardo Fosco Gramaccioni Alessandro Nicolosi Danilo Comminiello
In recent years, remarkable advancements have been achieved in the field of
image generation, primarily driven by the escalating demand for high-quality
outcomes across various image generation subtasks, such as inpainting,
denoising, and super resolution. A major effort is devoted to exploring the
application of super-resolution techniques to enhance the quality of
low-resolution images. In this context, our method explores in depth the
problem of ship image super resolution, which is crucial for coastal and port
surveillance. We investigate the opportunity given by the growing interest in
text-to-image diffusion models, taking advantage of the prior knowledge that
such foundation models have already learned. In particular, we present a
diffusion-model-based architecture that leverages text conditioning during
training while being class-aware, to best preserve the crucial details of the
ships during the generation of the super-resoluted image. Since the specificity
of this task and the scarcity availability of off-the-shelf data, we also
introduce a large labeled ship dataset scraped from online ship images, mostly
from ShipSpotting\footnote{\url{www.shipspotting.com}} website. Our method
achieves more robust results than other deep learning models previously
employed for super resolution, as proven by the multiple experiments performed.
Moreover, we investigate how this model can benefit downstream tasks, such as
classification and object detection, thus emphasizing practical implementation
in a real-world scenario. Experimental results show flexibility, reliability,
and impressive performance of the proposed framework over state-of-the-art
methods for different tasks. The code is available at:
https://github.com/LuigiSigillo/ShipinSight .