Ship in Sight: Diffusion Models for Ship-Image Super Resolution
<|reference_start|>Ship in Sight: Diffusion Models for Ship-Image Super Resolution: 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 .<|reference_end|>
@article{sigillo2024ship,
    title={Ship in Sight: Diffusion Models for Ship-Image Super Resolution},
    author={Luigi Sigillo, Riccardo Fosco Gramaccioni, Alessandro Nicolosi, Danilo
  Comminiello},
    journal={arXiv preprint arXiv:2403.18370},
    year={2024},
    doi={10.1109/IJCNN60899.2024.10650042},
    archivePrefix={arXiv},
    eprint={2403.18370},
    primaryClass={cs.CV cs.LG}
}