The learning-based super-resolution reconstruction method inputs a low-resolution image into a network, and learns a non-linear mapping relationship between low-resolution and high-resolution through ...the network. In this study, the multi-scale super-resolution reconstruction network is used to fuse the effective features of different scale images, and the non-linear mapping between low resolution and high resolution is studied from coarse to fine to realise the end-to-end super-resolution reconstruction task. The loss of some features of the low-resolution image will negatively affect the quality of the reconstructed image. To solve the problem of incomplete image features in low-resolution, this study adopts the multi-scale super-resolution reconstruction method based on guided image filtering. The high-resolution image reconstructed by the multi-scale super-resolution network and the real high-resolution image are merged by the guide image filter to generate a new image, and the newly generated image is used for secondary training of the multi-scale super-resolution reconstruction network. The newly generated image effectively compensates for the details and texture information lost in the low-resolution image, thereby improving the effect of the super-resolution reconstructed image.Compared with the existing super-resolution reconstruction scheme, the accuracy and speed of super-resolution reconstruction are improved.
In troubled societies narratives about the past tend to be partial and explain a conflict from narrow perspectives that justify the national self and condemn, exclude and devalue the 'enemy' and ...their narrative. Through a detailed analysis, Teaching Contested Narratives reveals the works of identity, historical narratives and memory as these are enacted in classroom dialogues, canonical texts and school ceremonies. Presenting ethnographic data from local contexts in Cyprus and Israel, and demonstrating the relevance to educational settings in countries which suffer from conflicts all over the world, the authors explore the challenges of teaching narratives about the past in such societies, discuss how historical trauma and suffering are dealt with in the context of teaching, and highlight the potential of pedagogical interventions for reconciliation. The book shows how the notions of identity, memory and reconciliation can perpetuate or challenge attachments to essentialized ideas about peace and conflict.
Many applications have benefited from the images with both high spatial and spectral resolution, such as mineralogy and surveillance. However, it is difficult to acquire such images due to the ...limitation of sensor technologies. Recently, super-resolution (SR) techniques have been proposed to improve the spatial or spectral resolution of images, e.g., improving the spatial resolution of hyperspectral images (HSIs) or improving spectral resolution of color images (reconstructing HSIs from RGB inputs). However, none of the researches attempted to improve both spatial and spectral resolution together. In this article, these two types of resolution are jointly improved using convolutional neural network (CNN). Specifically, two kinds of CNN-based SR are conducted, including a simultaneous spatial-spectral joint SR (SimSSJSR) that conducts SR in spectral and spatial domain simultaneously and a separated spatial-spectral joint SR (SepSSJSR) that considers spectral and spatial SR sequentially. In the proposed SimSSJSR, a full 3-D CNN is constructed to learn an end-to-end mapping between a low spatial-resolution mulitspectral image (LR-MSI) and the corresponding high spatial-resolution HSI (HR-HSI). In the proposed SepSSJSR, a spatial SR network and a spectral SR network are designed separately, and thus two different frameworks are proposed for SepSSJSR, namely SepSSJSR1 and SepSSJSR2, according to the order that spatial SR and spectral SR are applied. Furthermore, the least absolute deviation, instead of mean square error (MSE) in traditional SR networks, is chosen as the loss function for the proposed networks. Experimental results over simulated images from different sensors demonstrated that the proposed SepSSJSR1 is most effective to improve spatial and spectral resolution of MSIs sequentially by conducting spatial SR prior to spectral SR. In addition, validation on real Landsat images also indicates that the proposed SSJSR techniques can make full use of available MSIs for high-resolution-based analysis or applications.
This paper presents a hypserspectral image (HSI) super-resolution method, which fuses a low-resolution HSI (LR-HSI) with a high-resolution multispectral image (HR-MSI) to get high-resolution HSI ...(HR-HSI). The proposed method first extracts the nonlocal similar patches to form a nonlocal patch tensor (NPT). A novel tensor-tensor product (t - product)-based tensor sparse representation is proposed to model the extracted NPTs. Through the tensor sparse representation, both the spectral and spatial similarities between the nonlocal similar patches are well preserved. Then, the relationship between the HR-HSI and the LR-HSI is built using t - product, which allows us to design a unified objective function to incorporate the nonlocal similarity, tensor dictionary learning, and tensor sparse coding together. Finally, alternating direction method of multipliers is used to solve the optimization problem. Experimental results on three data sets and one real data set demonstrate that the proposed method substantially outperforms the existing state-ofthe-art HSI super-resolution methods.
Remote sensing technology has undeniable importance in various industrial applications, such as mineral exploration, plant detection, defect detection in aerospace and shipbuilding, and optical gas ...imaging, to name a few. Remote sensing technology has been continuously evolving, offering a range of image modalities that can facilitate the aforementioned applications. One such modality is Hyperspectral Imaging (HSI). Unlike Multispectral Images (MSI) and natural images, HSI consist of hundreds of bands. Despite their high spectral resolution, HSI suffer from low spatial resolution in comparison to their MSI counterpart, which hinders the utilization of their full potential. Therefore, spatial enhancement, or Super Resolution (SR), of HSI is a classical problem that has been gaining rapid attention over the past two decades. The literature is rich with various SR algorithms that enhance the spatial resolution of HSI while preserving their spectral fidelity. This paper reviews and discusses the most important algorithms relevant to this area of research between 2002-2022, along with the most frequently used datasets, HSI sensors, and quality metrics. Meta-analysis are drawn based on the aforementioned information, which is used as a foundation that summarizes the state of the field in a way that bridges the past and the present, identifies the current gap in it, and recommends possible future directions.
Identifying and visualizing vasculature within organs and tumors has major implications in managing cardiovascular diseases and cancer. Contrast-enhanced ultrasound scans detect slow-flowing blood, ...facilitating noninvasive perfusion measurements. However, their limited spatial resolution prevents the depiction of microvascular structures. Recently, super-localization ultrasonography techniques have surpassed this limit. However, they require long acquisition times of several minutes, preventing the detection of hemodynamic changes. We present a fast super-resolution method that exploits sparsity in the underlying vasculature and statistical independence within the measured signals. Similar to super-localization techniques, this approach improves the spatial resolution by up to an order of magnitude compared to standard scans. Unlike super-localization methods, it requires acquisition times of only tens of milliseconds. We demonstrate a temporal resolution of ~25 Hz, which may enable functional super-resolution imaging deep within the tissue, surpassing the temporal resolution limitations of current super-resolution methods, e.g., in neural imaging. The subsecond acquisitions make our approach robust to motion artifacts, simplifying in vivo use of super-resolution ultrasound.
Abstract
Using the Keck Planet Imager and Characterizer, we obtained high-resolution (
R
∼ 35,000)
K
-band spectra of the four planets orbiting HR 8799. We clearly detected H
2
O and CO in the ...atmospheres of HR 8799 c, d, and e, and tentatively detected a combination of CO and H
2
O in b. These are the most challenging directly imaged exoplanets that have been observed at high spectral resolution to date when considering both their angular separations and flux ratios. We developed a forward-modeling framework that allows us to jointly fit the spectra of the planets and the diffracted starlight simultaneously in a likelihood-based approach and obtained posterior probabilities on their effective temperatures, surface gravities, radial velocities, and spins. We measured
v
sin
(
i
)
values of
10.1
−
2.7
+
2.8
km
s
−
1
for HR 8799 d and
15.0
−
2.6
+
2.3
km
s
−
1
for HR 8799 e, and placed an upper limit of <14 km s
−1
of HR 8799 c. Under two different assumptions of their obliquities, we found tentative evidence that rotation velocity is anticorrelated with companion mass, which could indicate that magnetic braking with a circumplanetary disk at early times is less efficient at spinning down lower-mass planets.
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial ...resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to rely on deep neural networks for presenting the contextual information contained in different types of land-covers and propose a pseudo-labeling and sample selection scheme for improving the transferability of deep models. More precisely, a deep Convolutional Neural Networks (CNNs) is first pre-trained with a well-annotated land-cover dataset, referred to as the source data. Then, given a target image with no labels, the pre-trained CNN model is utilized to classify the image in a patch-wise manner. The patches with high confidence are assigned with pseudo-labels and employed as the queries to retrieve related samples from the source data. The pseudo-labels confirmed with the retrieved results are regarded as supervised information for fine-tuning the pre-trained deep model. To obtain a pixel-wise land-cover classification with the target image, we rely on the fine-tuned CNN and develop a hybrid classification by combining patch-wise classification and hierarchical segmentation. In addition, we create a large-scale land-cover dataset containing 150 Gaofen-2 satellite images for CNN pre-training. Experiments on multi-source HRRS images, including Gaofen-2, Gaofen-1, Jilin-1, Ziyuan-3, Sentinel-2A, and Google Earth platform data, show encouraging results and demonstrate the applicability of the proposed scheme to land-cover classification with multi-source HRRS images.
•A method to learn transferable deep model for 5-class land-cover (LC) classification.•A labeled dataset consisting of 150 Gaofen-2 images for LC classification.•It improves LC classification performance about 20% using multi-source RS images.•The method shows good transferability on different sensors and geolocations.
The adoption of electronic commercial transactions has facilitated cross-border trade and business, but the complexity of determining the place of business and other connecting factors in cyberspace ...has challenged existing private international law. This comparison of the rules of internet jurisdiction and choice of law as well as online dispute resolution (ODR) covers both B2B and B2C contracts in the EU, USA and China. It highlights the achievement of the Rome I Regulation in the EU, evaluates the merits of the Hague Convention on Choice of Court Agreement at the international level and gives an insight into the current developments in CIDIP. The in-depth research allows for solutions to be proposed relating to the problems of the legal uncertainty of internet conflict of law and the validity and enforceability of ODR agreements and decisions.
This article focuses on hyperspectral image (HSI) super-resolution that aims to fuse a low-spatial-resolution HSI and a high-spatial-resolution multispectral image to form a high-spatial-resolution ...HSI (HR-HSI). Existing deep learning-based approaches are mostly supervised that rely on a large number of labeled training samples, which is unrealistic. The commonly used model-based approaches are unsupervised and flexible but rely on handcrafted priors. Inspired by the specific properties of model, we make the first attempt to design a model-inspired deep network for HSI super-resolution in an unsupervised manner. This approach consists of an implicit autoencoder network built on the target HR-HSI that treats each pixel as an individual sample. The nonnegative matrix factorization (NMF) of the target HR-HSI is integrated into the autoencoder network, where the two NMF parts, spectral and spatial matrices, are treated as decoder parameters and hidden outputs, respectively. In the encoding stage, we present a pixelwise fusion model to estimate hidden outputs directly and then reformulate and unfold the model's algorithm to form the encoder network. With the specific architecture, the proposed network is similar to a manifold prior-based model and can be trained patch by patch rather than the entire images. Moreover, we propose an additional unsupervised network to estimate the point spread function and spectral response function. Experimental results conducted on both synthetic and real datasets demonstrate the effectiveness of the proposed approach.
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