7,676 research outputs found

    Physics-Driven Turbulence Image Restoration with Stochastic Refinement

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    Image distortion by atmospheric turbulence is a stochastic degradation, which is a critical problem in long-range optical imaging systems. A number of research has been conducted during the past decades, including model-based and emerging deep-learning solutions with the help of synthetic data. Although fast and physics-grounded simulation tools have been introduced to help the deep-learning models adapt to real-world turbulence conditions recently, the training of such models only relies on the synthetic data and ground truth pairs. This paper proposes the Physics-integrated Restoration Network (PiRN) to bring the physics-based simulator directly into the training process to help the network to disentangle the stochasticity from the degradation and the underlying image. Furthermore, to overcome the ``average effect" introduced by deterministic models and the domain gap between the synthetic and real-world degradation, we further introduce PiRN with Stochastic Refinement (PiRN-SR) to boost its perceptual quality. Overall, our PiRN and PiRN-SR improve the generalization to real-world unknown turbulence conditions and provide a state-of-the-art restoration in both pixel-wise accuracy and perceptual quality. Our codes are available at \url{https://github.com/VITA-Group/PiRN}.Comment: Accepted by ICCV 202

    Simulations of ELT-GMCAO performance for deep field observations

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    The Global-Multi Conjugated Adaptive Optics (GMCAO) approach offers an alternative way to correct an adequate scientific Field of View (FoV) using only natural guide stars (NGSs) to extremely large ground-based telescopes. Thus, even in the absence of laser guide stars, a GMCAO-equipped ELT-like telescope can achieve optimal performance in terms of Strehl Ratio (SR), retrieving impressive results in studying star-poor fields, as in the cases of the deep field observations. The benefits and usability of GMCAO have been demonstrated by studying 6000 mock high redshift galaxies in the Chandra Deep Field South region. However, a systematic study simulating observations in several portions of the sky is mandatory to have a robust statistic of the GMCAO performance. Technical, tomographic and astrophysical parameters, discussed here, are given as inputs to GIUSTO, an IDL-based code that estimates the SR over the considered field, and the results are analyzed with statistical considerations. The best performance is obtained using stars that are relatively close to the Scientific FoV; therefore, the SR correlates with the mean off-axis position of NGSs, as expected, while their magnitude plays a secondary role. This study concludes that the SRs correlate linearly with the galactic latitude, as also expected. Because of the lack of natural guide stars needed for low-order aberration sensing, the GMCAO confirms as a promising technique to observe regions that can not be studied without the use of laser beacons. It represents a robust alternative way or a risk mitigation strategy for laser approaches on the ELTs.Comment: 18 pages, 10 figures, accepted for publication on PAS

    Turbulence Characterisation for Free Space Optical Communication Using Off-Axis Digital Holography

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    An optical turbulence generator is characterised using digital holography, measuring the amplitude and phase of the perturbed optical field and enabling analysis of turbulence effects and development of mitigation techniques

    Single Frame Atmospheric Turbulence Mitigation: A Benchmark Study and A New Physics-Inspired Transformer Model

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    Image restoration algorithms for atmospheric turbulence are known to be much more challenging to design than traditional ones such as blur or noise because the distortion caused by the turbulence is an entanglement of spatially varying blur, geometric distortion, and sensor noise. Existing CNN-based restoration methods built upon convolutional kernels with static weights are insufficient to handle the spatially dynamical atmospheric turbulence effect. To address this problem, in this paper, we propose a physics-inspired transformer model for imaging through atmospheric turbulence. The proposed network utilizes the power of transformer blocks to jointly extract a dynamical turbulence distortion map and restore a turbulence-free image. In addition, recognizing the lack of a comprehensive dataset, we collect and present two new real-world turbulence datasets that allow for evaluation with both classical objective metrics (e.g., PSNR and SSIM) and a new task-driven metric using text recognition accuracy. Both real testing sets and all related code will be made publicly available.Comment: This paper is accepted as a poster at ECCV 202

    Atmospheric turbulence mitigation for sequences with moving objects using recursive image fusion

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    This paper describes a new method for mitigating the effects of atmospheric distortion on observed sequences that include large moving objects. In order to provide accurate detail from objects behind the distorting layer, we solve the space-variant distortion problem using recursive image fusion based on the Dual Tree Complex Wavelet Transform (DT-CWT). The moving objects are detected and tracked using the improved Gaussian mixture models (GMM) and Kalman filtering. New fusion rules are introduced which work on the magnitudes and angles of the DT-CWT coefficients independently to achieve a sharp image and to reduce atmospheric distortion, respectively. The subjective results show that the proposed method achieves better video quality than other existing methods with competitive speed.Comment: IEEE International Conference on Image Processing 201

    Object recognition in atmospheric turbulence scenes

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    The influence of atmospheric turbulence on acquired surveillance imagery poses significant challenges in image interpretation and scene analysis. Conventional approaches for target classification and tracking are less effective under such conditions. While deep-learning-based object detection methods have shown great success in normal conditions, they cannot be directly applied to atmospheric turbulence sequences. In this paper, we propose a novel framework that learns distorted features to detect and classify object types in turbulent environments. Specifically, we utilise deformable convolutions to handle spatial turbulent displacement. Features are extracted using a feature pyramid network, and Faster R-CNN is employed as the object detector. Experimental results on a synthetic VOC dataset demonstrate that the proposed framework outperforms the benchmark with a mean Average Precision (mAP) score exceeding 30%. Additionally, subjective results on real data show significant improvement in performance

    On the determination of the atmospheric outer scale length of turbulence using GPS phase difference observations : The Seewinkel network

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    Microwave electromagnetic signals from the Global Navigation Satellite System (GNSS) are affected by their travel through the atmosphere: the troposphere, a non-dispersive medium, has an especial impact on the measurements. The long-term variations of the tropospheric refractive index delay the signals, whereas its random variations correlate with the phase measurements. The correlation structure of residuals from GNSS relative position estimation provides a unique opportunity to study specific properties of the turbulent atmosphere. Prior to such a study, the residuals have to be filtered from unwanted additional effects, such as multipath. In this contribution, we propose to investigate the property of the atmospheric noise by using a new methodology combining the empirical mode decomposition with the Hilbert–Huang transform. The chirurgical “designalling of the noise” aims to filter both the white noise and low-frequency noise to extract only the noise coming from tropospheric turbulence. Further analysis of the power spectrum of phase difference can be performed, including the study of the cut-off frequencies and the two slopes of the power spectrum of phase differences. The obtained values can be compared with theoretical expectations. In this contribution, we use Global Positioning System (GPS) phase observations from the Seewinkel network, specially designed to study the impact of atmospheric turbulence on GPS phase observations. We show that (i) a two-slope power spectrum can be found in the residuals and (ii) that the outer scale length can be taken to a constant value, close to the physically expected one and in relation with the size of the eddies at tropospheric height.[Figure not available: see fulltext.] © 2020, The Author(s)
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