Single image super-resolution (SR) has been widely studied in recent years as a crucial technique for remote sensing applications. This paper proposes a SR method for remote sensing images based on a transferred gener...
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Single image super-resolution (SR) has been widely studied in recent years as a crucial technique for remote sensing applications. This paper proposes a SR method for remote sensing images based on a transferred generative adversarial network (TGAN). Different from the previous GAN-based SR approaches, the novelty of our method mainly reflects from two aspects. First, the batch normalization layers are removed to reduce the memory consumption and the computational burden, as well as raising the accuracy. Second, our model is trained in a transfer-learning fashion to cope with the insufficiency of training data, which is the crux of applying deep learning methods to remote sensing applications. The model is firstly trained on an external dataset DIV2K and further fine-tuned with the remote sensing dataset. Our experimental results demonstrate that the proposed method is superior to SRCNN and SRGAN in terms of both the objective evaluation and the subjective perspective.
Aiming at the high dimension of the characteristic of partial discharge and its high sensitivity to noise, firstly, the Synchrosqueezing wavelet transform is used to decompose the four typical partial discharge signal...
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Aiming at the high dimension of the characteristic of partial discharge and its high sensitivity to noise, firstly, the Synchrosqueezing wavelet transform is used to decompose the four typical partial discharge signals of transformers to overcome the defects of spectrum aliasing and energy leakage between real wavelet packet decomposition sub-bands.;Then, using the difference of energy and complexity of PD signals at different decomposition scales, the parameters of multi-scale energy and multi-scale energy spectrum entropy are used as the feature quantity of discharge type identification;Finally, the extracted features support vector machine classifier for discharge pattern recognition. Experimental results show, the proposed method can achieve better recognition than EMD and wavelet packet decomposition, and proves the effectiveness of the proposed method.
The theoretical modeling and analysis of SAR location error play an important role in SAR system design and error source budget. Existing SAR geolocation error models are mainly implicit, which are not easy to do anal...
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A composite network formed by factor networks is a new type of complex network model that can describe different types of agents and multiple complicated relationships in complex systems. "Indu-Bala product"...
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A composite network formed by factor networks is a new type of complex network model that can describe different types of agents and multiple complicated relationships in complex systems. "Indu-Bala product" of graphs is a new graph operation with symmetry properties and good mathematical expression, which enables one to construct many pairs of respective co-spectral graphs. This work studies the controllability of a composite network formed by factor networks via Indu-Bala product based on Laplacian dynamics. The eigenvalues and eigenvectors of Indu-Bala product networks (IBPNs) can be represented by factors' eigenvalues and eigenvectors, which can reduce the computational complexity and analytical burden. Based on the PBH test, sufficient and necessary algebraic criterions for controllability of IBPNs are established, which reveal the controllability relationship between entire network and its factors. This work will help one to construct, understand and improve the composite complex network models, and gain insight into the synergistic mechanisms of the networks.
Interferometric synthetic aperture radar (InSAR) can be used to extract digital elevation model (DEM) with high accuracy. However, the side looking geometry of synthetic aperture radar (SAR) may cause geometric distor...
ISBN:
(数字)9781728129129
ISBN:
(纸本)9781728129136
Interferometric synthetic aperture radar (InSAR) can be used to extract digital elevation model (DEM) with high accuracy. However, the side looking geometry of synthetic aperture radar (SAR) may cause geometric distortions such as shadow and layover in the mountainous terrain, which will reduce the quality of generated DEM. Fusion of two or more different aspects of InSAR data can deal with this problem. We propose an InSAR DEM reconstruction method based on backprojection (BP) algorithm in two converse flights. This method utilizes the feature of BP algorithm that geocoding has been realized in imaging process to simplify the fusion process of multi-aspect InSAR data. In addition, an iterative DEM extraction method is introduced to improve DEM accuracy. Experimental results verify the effectiveness of the proposed method.
Semantic information is important in video encryption. However, existing image quality assessment (IQA) methods, such as the peak signal to noise ratio (PSNR), are still widely applied to measure the encryption securi...
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Semantic information is important in video encryption. However, existing image quality assessment (IQA) methods, such as the peak signal to noise ratio (PSNR), are still widely applied to measure the encryption security. Generally, these traditional IQA methods aim to evaluate the image quality from the perspective of visual signal rather than semantic information. In this paper, we propose a novel semantic-level full-reference image quality assessment (FR-IQA) method named Semantic Distortion Measurement (SDM) to measure the degree of semantic distortion for video encryption. Then, based on a semantic saliency dataset, we verify that the proposed SDM method outperforms state-of-the-art algorithms. Furthermore, we construct a Region Of Semantic Saliency (ROSS) video encryption system to demonstrate the effectiveness of our proposed SDM method in the practical application.
One key challenge to the learning-based image compression is that adaptive bit allocation is crucial for compression effectiveness but can hardly be trained into a neural network. Hereby, in this work, We presents an ...
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ISBN:
(纸本)9781538644591;9781538644584
One key challenge to the learning-based image compression is that adaptive bit allocation is crucial for compression effectiveness but can hardly be trained into a neural network. Hereby, in this work, We presents an end-to-end trainable image compression framework, named Multi-scale Progressive Network (MPN) to achieve spatially variant bit allocation and rate control through the guidance of a novel learnable just noticeable distortion (JND) map. Specifically, MPN's encoder archives multi-scale feature representation through a three-branched structure. Each branch employs an independent feature extraction strategy for the specific receptive field and merge progressively under the guidance of corresponding learnable JND maps that generated by our proposed Bit-Allocation sub-Network (BAN), which make MPN focus on the areas where attract the human visual system (HVS) and preserve more texture of the image during the compression procedure. Finally, a hybrid objective function is introduced to further make MPN more efficient and mimic the discriminative characteristics of the human visual system (HVS). Experiments show that MPN significantly outperforms traditional JPEG, JPEG 2000 and few state-of-art learning-based methods by multi-scale structural similarity (MS-SSIM) index, and has the ability to produce the much better visual result with rich textures, sharp edges, and fewer artifacts.
Inspired by the progress of image and video super-resolution (SR) achieved by convolutional neural network (CNN), we propose a CNN-based residue SR method for video coding. Different from the previous works that opera...
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ISBN:
(纸本)9781538644591;9781538644584
Inspired by the progress of image and video super-resolution (SR) achieved by convolutional neural network (CNN), we propose a CNN-based residue SR method for video coding. Different from the previous works that operate in the pixel domain, i.e. down- and up-sampling of image or video frame, we propose to perform down- and up-sampling in the residue domain. Specifically, for each block, we perform motion estimation and compensation to achieve residual signal at the original resolution, then we down-sample the residue and compress it at low resolution, and perform residue SR using a trained CNN model. We design a new CNN for residue SR with the help of the motion compensated prediction signal. We integrate the residue SR method into the High Efficiency Video Coding (HEVC) scheme, providing mode decision at the level of coding tree unit. Experimental results show that our method achieves on average 4.0% and 2.8% BD-rate reduction under low-delay P and low-delay B configurations, respectively.
In this paper, we propose a learned scalable/progressive image compression scheme based on deep neural networks (DNN), named Bidirectional Context Disentanglement Network (BCD-Net). For learning hierarchical represent...
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Motion estimation and motion compensation are fundamental in video coding to remove the temporal redundancy between video frames. The current video coding schemes usually adopt block-based motion estimation and compen...
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ISBN:
(纸本)9781538644591;9781538644584
Motion estimation and motion compensation are fundamental in video coding to remove the temporal redundancy between video frames. The current video coding schemes usually adopt block-based motion estimation and compensation using simple translational or affine motion models, which cannot efficiently characterize complex motions in natural video signal. In this paper, we propose a frame extrapolation method for motion estimation and compensation. Specifically, based on the several previous frames, our method directly extrapolates the current frame using a trained deep network model. The deep network we adopted is a redesigned Video Coding oriented LAplacian Pyramid of Generative Adversarial Networks (VC-LAPGAN). The extrapolated frame is then used as an additional reference frame. Experimental results show that the VC-LAPGAN is capable in estimating and compensating for complex motions, and extrapolating frames with high visual quality. Using the VC-LAPGAN, our method achieves on average 2.0% BD-rate reduction than High Efficiency Video Coding (HEVC) under low-delay P configuration.
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