386 research outputs found
Real-time Model-based Image Color Correction for Underwater Robots
Recently, a new underwater imaging formation model presented that the
coefficients related to the direct and backscatter transmission signals are
dependent on the type of water, camera specifications, water depth, and imaging
range. This paper proposes an underwater color correction method that
integrates this new model on an underwater robot, using information from a
pressure depth sensor for water depth and a visual odometry system for
estimating scene distance. Experiments were performed with and without a color
chart over coral reefs and a shipwreck in the Caribbean. We demonstrate the
performance of our proposed method by comparing it with other statistic-,
physic-, and learning-based color correction methods. Applications for our
proposed method include improved 3D reconstruction and more robust underwater
robot navigation.Comment: Accepted at the 2019 IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS
Full-Scale Continuous Synthetic Sonar Data Generation with Markov Conditional Generative Adversarial Networks
Deployment and operation of autonomous underwater vehicles is expensive and
time-consuming. High-quality realistic sonar data simulation could be of
benefit to multiple applications, including training of human operators for
post-mission analysis, as well as tuning and validation of autonomous target
recognition (ATR) systems for underwater vehicles. Producing realistic
synthetic sonar imagery is a challenging problem as the model has to account
for specific artefacts of real acoustic sensors, vehicle altitude, and a
variety of environmental factors. We propose a novel method for generating
realistic-looking sonar side-scans of full-length missions, called Markov
Conditional pix2pix (MC-pix2pix). Quantitative assessment results confirm that
the quality of the produced data is almost indistinguishable from real.
Furthermore, we show that bootstrapping ATR systems with MC-pix2pix data can
improve the performance. Synthetic data is generated 18 times faster than real
acquisition speed, with full user control over the topography of the generated
data.Comment: 6 pages, 6 figures. Accepted to ICRA2020. 2020 IEEE International
Conference on Robotics and Automatio
Real-World Image Restoration Using Degradation Adaptive Transformer-Based Adversarial Network
Most existing learning-based image restoration methods heavily rely on paired degraded/non-degraded training datasets that are based on simplistic handcrafted degradation assumptions. These assumptions often involve a limited set of degradations, such as Gaussian blurs, noises, and bicubic downsampling. However, when these methods are applied to real-world images, there is a significant decrease in performance due to the discrepancy between synthetic and realistic degradation. Additionally, they lack the flexibility to adapt to unknown degradations in practical scenarios, which limits their generalizability to complex and unconstrained scenes.
To address the absence of image pairs, recent studies have proposed Generative Adversarial Network (GAN)-based unpaired methods. Nevertheless, unpaired learning models based on convolution operations encounter challenges in capturing long-range pixel dependencies in real-world images. This limitation stems from their reliance on convolution operations, which offer local connectivity and translation equivariance but struggle to capture global dependencies due to their limited receptive field.
To address these challenges, this dissertation proposed an innovative unpaired image restoration basic model along with an advanced model. The proposed basic model is the DA-CycleGAN model, which is based on the CycleGAN [1] neural network and specifically designed for blind real-world Single Image Super-Resolution (SISR). The DA-CycleGAN incorporates a degradation adaptive (DA) module to learn various real-world degradations (such as noise and blur patterns) in an unpaired manner, enabling strong flexible adaptation. Additionally, an advanced model called Trans-CycleGAN was designed, which integrated the Transformer architecture into CycleGAN to leverage its global connectivity. This combination allowed for image-to-image translation using CycleGAN [1] while enabling the Transformer to model global connectivity across long-range pixels. Extensive experiments conducted on realistic images demonstrate the superior performance of the proposed method in solving real-world image restoration problems, resulting in clearer and finer details.
Overall, this dissertation presents a novel unpaired image restoration basic model and an advanced model that effectively address the limitations of existing approaches. The proposed approach achieves significant advancements in handling real-world degradations and modeling long-range pixel dependencies, thereby offering substantial improvements in image restoration tasks.
Index Terms— Cross-domain translation, generative adversarial network, image restoration, super-resolution, transformer, unpaired training
Underwater Image Super-Resolution using Deep Residual Multipliers
We present a deep residual network-based generative model for single image
super-resolution (SISR) of underwater imagery for use by autonomous underwater
robots. We also provide an adversarial training pipeline for learning SISR from
paired data. In order to supervise the training, we formulate an objective
function that evaluates the \textit{perceptual quality} of an image based on
its global content, color, and local style information. Additionally, we
present USR-248, a large-scale dataset of three sets of underwater images of
'high' (640x480) and 'low' (80x60, 160x120, and 320x240) spatial resolution.
USR-248 contains paired instances for supervised training of 2x, 4x, or 8x SISR
models. Furthermore, we validate the effectiveness of our proposed model
through qualitative and quantitative experiments and compare the results with
several state-of-the-art models' performances. We also analyze its practical
feasibility for applications such as scene understanding and attention modeling
in noisy visual conditions
Underwater Image Super-Resolution using Generative Adversarial Network-based Model
Single image super-resolution (SISR) models are able to enhance the
resolution and visual quality of underwater images and contribute to a better
understanding of underwater environments. The integration of these models in
Autonomous Underwater Vehicles (AUVs) can improve their performance in
vision-based tasks. Real-Enhanced Super-Resolution Generative Adversarial
Network (Real-ESRGAN) is an efficient model that has shown remarkable
performance among SISR models. In this paper, we fine-tune the pre-trained
Real-ESRGAN model for underwater image super-resolution. To fine-tune and
evaluate the performance of the model, we use the USR-248 dataset. The
fine-tuned model produces more realistic images with better visual quality
compared to the Real-ESRGAN model
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