Unlimited Resolution Image Generation with R2D2-GANs

• URL: http://arxiv.org/abs/2003.01063v1
• Date: Mon, 2 Mar 2020 17:49:32 GMT
• Title: Unlimited Resolution Image Generation with R2D2-GANs
• Authors: Marija Jegorova, Antti Ilari Karjalainen, Jose Vazquez, Timothy M. Hospedales
• Abstract summary: We present a novel simulation technique for generating high quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission. The data produced is continuous, realistically-looking, and can also be generated at least two times faster than the real speed of acquisition.
• Score: 69.90258455164513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper we present a novel simulation technique for generating high quality images of any predefined resolution. This method can be used to synthesize sonar scans of size equivalent to those collected during a full-length mission, with across track resolutions of any chosen magnitude. In essence, our model extends Generative Adversarial Networks (GANs) based architecture into a conditional recursive setting, that facilitates the continuity of the generated images. The data produced is continuous, realistically-looking, and can also be generated at least two times faster than the real speed of acquisition for the sonars with higher resolutions, such as EdgeTech. The seabed topography can be fully controlled by the user. The visual assessment tests demonstrate that humans cannot distinguish the simulated images from real. Moreover, experimental results suggest that in the absence of real data the autonomous recognition systems can benefit greatly from training with the synthetic data, produced by the R2D2-GANs.

Related papers

• Time Step Generating: A Universal Synthesized Deepfake Image Detector [0.4488895231267077]
  We propose a universal synthetic image detector Time Step Generating (TSG) TSG does not rely on pre-trained models' reconstructing ability, specific datasets, or sampling algorithms. We test the proposed TSG on the large-scale GenImage benchmark and it achieves significant improvements in both accuracy and generalizability.
  arXiv  Detail & Related papers  (2024-11-17T09:39:50Z)
• bit2bit: 1-bit quanta video reconstruction via self-supervised photon prediction [57.199618102578576]
  We propose bit2bit, a new method for reconstructing high-quality image stacks at original resolution from sparse binary quantatemporal image data. Inspired by recent work on Poisson denoising, we developed an algorithm that creates a dense image sequence from sparse binary photon data. We present a novel dataset containing a wide range of real SPAD high-speed videos under various challenging imaging conditions.
  arXiv  Detail & Related papers  (2024-10-30T17:30:35Z)
• Towards Realistic Data Generation for Real-World Super-Resolution [58.88039242455039]
  RealDGen is an unsupervised learning data generation framework designed for real-world super-resolution. We develop content and degradation extraction strategies, which are integrated into a novel content-degradation decoupled diffusion model. Experiments demonstrate that RealDGen excels in generating large-scale, high-quality paired data that mirrors real-world degradations.
  arXiv  Detail & Related papers  (2024-06-11T13:34:57Z)
• Creating Realistic Anterior Segment Optical Coherence Tomography Images using Generative Adversarial Networks [0.0]
  Generative Adversarial Network (GAN) purposed to create high-resolution, realistic Anterior Segment Optical Coherence Tomography (AS- OCT) images. We trained the Style and WAvelet based GAN on 142,628 AS- OCT B-scans.
  arXiv  Detail & Related papers  (2023-06-24T20:48:00Z)
• Passive superresolution imaging of incoherent objects [63.942632088208505]
  Method consists of measuring the field's spatial mode components in the image plane in the overcomplete basis of Hermite-Gaussian modes and their superpositions. Deep neural network is used to reconstruct the object from these measurements.
  arXiv  Detail & Related papers  (2023-04-19T15:53:09Z)
• A Model-data-driven Network Embedding Multidimensional Features for Tomographic SAR Imaging [5.489791364472879]
  We propose a new model-data-driven network to achieve tomoSAR imaging based on multi-dimensional features. We add two 2D processing modules, both convolutional encoder-decoder structures, to enhance multi-dimensional features of the imaging scene effectively. Compared with the conventional CS-based FISTA method and DL-based gamma-Net method, the result of our proposed method has better performance on completeness while having decent imaging accuracy.
  arXiv  Detail & Related papers  (2022-11-28T02:01:43Z)
• Enhancement of Novel View Synthesis Using Omnidirectional Image Completion [61.78187618370681]
  We present a method for synthesizing novel views from a single 360-degree RGB-D image based on the neural radiance field (NeRF) Experiments demonstrated that the proposed method can synthesize plausible novel views while preserving the features of the scene for both artificial and real-world data.
  arXiv  Detail & Related papers  (2022-03-18T13:49:25Z)
• Unpaired Single-Image Depth Synthesis with cycle-consistent Wasserstein GANs [1.0499611180329802]
  Real-time estimation of actual environment depth is an essential module for various autonomous system tasks. In this study, latest advancements in the field of generative neural networks are leveraged to fully unsupervised single-image depth synthesis.
  arXiv  Detail & Related papers  (2021-03-31T09:43:38Z)
• Generating 3D structures from a 2D slice with GAN-based dimensionality expansion [0.0]
  Generative adversarial networks (GANs) can be trained to generate 3D image data, which is useful for design optimisation. We introduce a generative adversarial network architecture, SliceGAN, which is able to synthesise high fidelity 3D datasets using a single representative 2D image.
  arXiv  Detail & Related papers  (2021-02-10T18:46:17Z)
• Two-shot Spatially-varying BRDF and Shape Estimation [89.29020624201708]
  We propose a novel deep learning architecture with a stage-wise estimation of shape and SVBRDF. We create a large-scale synthetic training dataset with domain-randomized geometry and realistic materials. Experiments on both synthetic and real-world datasets show that our network trained on a synthetic dataset can generalize well to real-world images.
  arXiv  Detail & Related papers  (2020-04-01T12:56:13Z)

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.