Ship in Sight: Diffusion Models for Ship-Image Super Resolution

• URL: http://arxiv.org/abs/2403.18370v2
• Date: Tue, 21 May 2024 16:45:05 GMT
• Title: Ship in Sight: Diffusion Models for Ship-Image Super Resolution
• Authors: Luigi Sigillo, Riccardo Fosco Gramaccioni, Alessandro Nicolosi, Danilo Comminiello,
• Abstract summary: We present a diffusion-model-based architecture that leverages text conditioning during training while being class-aware. We also introduce a large labeled ship dataset scraped from online ship images, mostly from ShipSpottingfootnoteurlwww.shipspotting.com website. Our method achieves more robust results than other deep learning models previously employed for super resolution.
• Score: 45.618404722764694
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 .

Related papers

• Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation [53.95204595640208]
  Data-Free Knowledge Distillation (DFKD) is an advanced technique that enables knowledge transfer from a teacher model to a student model without relying on original training data. Previous approaches have generated synthetic images at high resolutions without leveraging information from real images. MUSE generates images at lower resolutions while using Class Activation Maps (CAMs) to ensure that the generated images retain critical, class-specific features.
  arXiv  Detail & Related papers  (2024-11-26T02:23:31Z)
• DiffuseHigh: Training-free Progressive High-Resolution Image Synthesis through Structure Guidance [11.44012694656102]
  Large-scale generative models, such as text-to-image diffusion models, have garnered widespread attention across diverse domains. Existing large-scale diffusion models are confined to generating images of up to 1K resolution. We propose a novel progressive approach that fully utilizes generated low-resolution images to guide the generation of higher-resolution images.
  arXiv  Detail & Related papers  (2024-06-26T16:10:31Z)
• Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers [120.49126407479717]
  This paper explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) We highlight a pivotal discovery: the capacity of text-to-image diffusion models to seamlessly bridge the gap between sketches and photos.
  arXiv  Detail & Related papers  (2024-03-12T00:02:03Z)
• ScaleCrafter: Tuning-free Higher-Resolution Visual Generation with Diffusion Models [126.35334860896373]
  We investigate the capability of generating images from pre-trained diffusion models at much higher resolutions than the training image sizes. Existing works for higher-resolution generation, such as attention-based and joint-diffusion approaches, cannot well address these issues. We propose a simple yet effective re-dilation that can dynamically adjust the convolutional perception field during inference.
  arXiv  Detail & Related papers  (2023-10-11T17:52:39Z)
• Flow Matching in Latent Space [2.9330609943398525]
  Flow matching is a framework to train generative models that exhibits impressive empirical performance. We propose to apply flow matching in the latent spaces of pretrained autoencoders, which offers improved computational efficiency. Our work stands as a pioneering contribution in the integration of various conditions into flow matching for conditional generation tasks.
  arXiv  Detail & Related papers  (2023-07-17T17:57:56Z)
• Adaptive deep learning framework for robust unsupervised underwater image enhancement [3.0516727053033392]
  One of the main challenges in deep learning-based underwater image enhancement is the limited availability of high-quality training data. We propose a novel unsupervised underwater image enhancement framework that employs a conditional variational autoencoder (cVAE) to train a deep learning model. We show that our proposed framework yields competitive performance compared to other state-of-the-art approaches in quantitative as well as qualitative metrics.
  arXiv  Detail & Related papers  (2022-12-18T01:07:20Z)
• InvGAN: Invertible GANs [88.58338626299837]
  InvGAN, short for Invertible GAN, successfully embeds real images to the latent space of a high quality generative model. This allows us to perform image inpainting, merging, and online data augmentation.
  arXiv  Detail & Related papers  (2021-12-08T21:39:00Z)
• Image Completion via Inference in Deep Generative Models [16.99337751292915]
  We consider image completion from the perspective of amortized inference in an image generative model. We demonstrate superior sample quality and diversity compared to prior art on the CIFAR-10 and FFHQ-256 datasets.
  arXiv  Detail & Related papers  (2021-02-24T02:59:43Z)
• Unsupervised Real Image Super-Resolution via Generative Variational AutoEncoder [47.53609520395504]
  We revisit the classic example based image super-resolution approaches and come up with a novel generative model for perceptual image super-resolution. We propose a joint image denoising and super-resolution model via Variational AutoEncoder. With the aid of the discriminator, an additional overhead of super-resolution subnetwork is attached to super-resolve the denoised image with photo-realistic visual quality.
  arXiv  Detail & Related papers  (2020-04-27T13:49:36Z)
• Learning Enriched Features for Real Image Restoration and Enhancement [166.17296369600774]
  convolutional neural networks (CNNs) have achieved dramatic improvements over conventional approaches for image restoration task. We present a novel architecture with the collective goals of maintaining spatially-precise high-resolution representations through the entire network. Our approach learns an enriched set of features that combines contextual information from multiple scales, while simultaneously preserving the high-resolution spatial details.
  arXiv  Detail & Related papers  (2020-03-15T11:04:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.