We present the integral field unit part of the data reduction pipeline for METIS (Mid-infrared ELT imager and Spectrograph), a first-generation infrared instrument that will be installed on the Extremely Large Telesco...
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ISBN:
(纸本)9781510675261;9781510675254
We present the integral field unit part of the data reduction pipeline for METIS (Mid-infrared ELT imager and Spectrograph), a first-generation infrared instrument that will be installed on the Extremely Large Telescope. The described software covers the entire process of correcting the instrumental effects and reconstructing the hyperspectral image. Apart from standard correction procedures common to virtually all digital imagers, the pipeline includes methods for distortion calibration, wavelength and flux calibration, correction of telluric absorption, reconstruction of the spectral cube with special emphasis on resampling the data only once, and finally algorithms for spatial and spectral dithering of multiple exposures taken at different field orientations and shifts, possibly taken many months apart. The pipeline has already passed the final design review and its implementation is underway.
The features of aero-engine hollow turbine blades show a complicated inner air tract, which have a significant influence on the engine's performance. Inspection of the internal structure and flaws of the blades be...
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The features of aero-engine hollow turbine blades show a complicated inner air tract, which have a significant influence on the engine's performance. Inspection of the internal structure and flaws of the blades become indispensable. Non-destructive testing, such as computed tomography (CT), is an effective method for detecting internal problems. The purpose of this study is to demonstrate how an iterative excitation can be used to recover an incomplete projection in CT for a turbine blade. Firstly, the variance of the background was gathered as previous information. Then, to make up for the missed sample at the ill-angle position, a noise map was added and filtered as prep work for the forward projection. The original projections were retained, and the revised projections were used to extract additional characteristics from the damaged data. Finally, both simulation and actual tests were studied. The Normalised Mean Square Distance (NMSD) of the reconstructed image was reduced by over 20%. The Structural Similarity Index Measure (SSIM) and the Universal Quality Index (UQI) were both enhanced by at least 60% and 41%, respectively. It was demonstrated that the technique can increase the accuracy of reconstruction for a hollow turbine blade.
This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e.g., a torso. The core challenge arises from the occlusion: there exists no...
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ISBN:
(纸本)9798350301298
This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e.g., a torso. The core challenge arises from the occlusion: there exists no pixel to reconstruct where many existing single-view human reconstruction methods are not designed to handle such invisible parts, leading to missing data in 3D. To address this challenge, we introduce a novel coarse-to-fine human reconstruction framework. For coarse reconstruction, explicit volumetric features are learned to generate a complete human geometry with 3D convolutional neural networks conditioned by a 3D body model and the style features from visible parts. An implicit network combines the learned 3D features with the high-quality surface normals enhanced from multiviews to produce fine local details, e.g., high-frequency wrinkles. Finally, we perform progressive texture inpainting to reconstruct a complete appearance of the person in a view-consistent way, which is not possible without the reconstruction of a complete geometry. In experiments, we demonstrate that our method can reconstruct high-quality 3D humans, which is robust to occlusion.
In the context of nuclear safety production, the detailed information on three-dimensional radiation field dose rates plays a vital role in enhancing radiation protection and planning for radioactive areas. Research o...
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ISBN:
(纸本)9780791888285
In the context of nuclear safety production, the detailed information on three-dimensional radiation field dose rates plays a vital role in enhancing radiation protection and planning for radioactive areas. Research on inversion methods, especially under scenarios of sparse or irregular measurement points, has attracted significant attention. Currently, there are advances in inversion methods based on interpolation algorithms and neural network techniques. Interpolation algorithms are inversion methods capable of rapidly inverting and reconstructing the distribution of radiation field dose rates with limited radiation field data. These methods include grid data interpolation, finite element methods, inverse distance weighting, kriging, and radial basis function methods, and are the most widely used methods for the quick reconstruction of radiation fields at present. Based on the information of known sample points, these algorithms predict information at unsampled points by selecting appropriate analytical models to find suitable mapping relationships. They offer the advantages of simplicity and speed, but have poor capability in restoring the geometric shapes of multiple radiation sources and face significant challenges in the direct inversion and reconstruction of three-dimensional radiation fields. In some cases, sparse sampling data can lead to suboptimal inversion results. Neural network algorithms, renowned for their strong nonlinear mapping capabilities, can approximate complex functions by learning from extensive data sets. Once appropriately trained, neural networks can process incomplete or uncertain knowledge and predict outcomes based on limited data. These algorithms are commonly used in image restoration and inverse problem-solving. In recent radiation field dose rate inversion methods, neural network algorithms have gained attention due to their exceptional data fitting and predictive *** recent years, research has proposed an inversion met
With the rapid development of digital technology and deep learning, recovering 3D scene information and reconstructing human bodies from a single image has become a focal point of research in computer vision and compu...
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VLBI (very long baseline interferometry) is used to image astronomical objects. However, the image quality decreases due to incomplete visibility measurements. The visibility is affected by the frequency, baseline len...
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Masked image modeling is a promising self-supervised learning method for visual data. It is typically built upon image patches with random masks, which largely ignores the variation of information density between them...
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ISBN:
(纸本)9789819985425;9789819985432
Masked image modeling is a promising self-supervised learning method for visual data. It is typically built upon image patches with random masks, which largely ignores the variation of information density between them. The question is: Is there a better masking strategy than random sampling and how can we learn it? We empirically study this problem and initially find that introducing object-centric priors in mask sampling can significantly improve the learned representations. Inspired by this observation, we present AutoMAE, a fully differentiable framework that uses Gumbel-Softmax to interlink an adversarially trained mask generator and a mask-guided image modeling process. In this way, our approach can adaptively find patches with higher information density for different images, and further strike a balance between the information gain obtained fromimagereconstruction and its practical training difficulty. In our experiments, AutoMAE is shown to provide effective pretraining models on standard self-supervised benchmarks and downstream tasks.
Transformer has achieved significant progress in light field image super-resolution (LFSR) due to its long-range dependency learning ability for inter-intra view feature aggregation. However, locality information of e...
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ISBN:
(纸本)9789819786848;9789819786855
Transformer has achieved significant progress in light field image super-resolution (LFSR) due to its long-range dependency learning ability for inter-intra view feature aggregation. However, locality information of each sub-aperture view is ignored in intra-view and inter-view aggregation with Transformer, hampering the high-quality light field imagereconstruction. To this end, we propose a global to local aggregation approach termed Focal Aggregation for LFSR. In particular, Focal Aggregation includes two strategies: inter-view global to local aggregation (InterG2L) and intra-view global to local aggregation (IntraG2L). InterG2L is proposed to obtain complementary information from different views. IntraG2L is developed to extract efficient representations of a single sub-aperture view. InterG2L and IntraG2L are organized in a cascade way so that the global information of the input can be gathered for each sub-aperture image in a coarse to fine aggregation approach. Meanwhile, we also develop a global to local hierarchical feature aggregation approach named HierG2L, which enhances the last hierarchical feature used for light field reconstruction according to the input. Based on the above three global to local aggregation strategies, we construct a focal aggregation transformer (FAT) for LFSR. Experiments are performed on commonly-used LFSR benchmarks. Results demonstrate that FAT achieves superior results compared with other leading methods on synthesized and real data.
Photoacoustic imaging (PAI) offers significant advantages but faces challenges in data processing and reconstruction. Sparse reconstruction techniques and compressed sensing theory have advanced its development. Regul...
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Wide-field image correction of turbulence-induced phase requires tomographic reconstruction of each layer of turbulence. Before reconstruction can occur, the layers must be counted and ranged. A new signal-to-noise ra...
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ISBN:
(纸本)9781510653528;9781510653511
Wide-field image correction of turbulence-induced phase requires tomographic reconstruction of each layer of turbulence. Before reconstruction can occur, the layers must be counted and ranged. A new signal-to-noise ratio metric for detecting a single layer of turbulence in a multi-layer atmosphere from SLOpe Detection And Ranging (SLODAR) measurements of Shack-Hartmann wave-front sensor (SHWFS) data is presented. 12,000 1-4 layer atmosphere profiles are procedurally defined by Fried length, layer altitude, and a minimum layer SNR requirement. Each profile is measured in simulation by a SHWFS in a 1.5 meter telescope with a 2.5 arcminute field of view over a 200 millisecond window. The simulation outputs are used as a 5-fold cross validation training data set for convolutional neural networks (CNNs) that count and range layers. The counting network achieved 92.6% accuracy and all ranging networks scored above 97.8% validation accuracy. We find that layers with SNR below 1 accounted for a majority of the misclassified points for all networks. We conclude that CNNs are a good candidate for wide-field image correction systems imaging through turbulence due to their ability to accurately profile the atmosphere from short time windows of collected data.
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