Across the field of inverse problems in image and video processing, nearly all algorithms have various parameters which need to be set in order to yield good results. In practice, usually the choice ...of such parameters is made empirically with trial and error if no "ground-truth" reference is available. Some analytical methods such as cross-validation and Stein's unbiased risk estimate (SURE) have been successfully used to set such parameters. However, these methods tend to be strongly reliant on restrictive assumptions on the noise, and also computationally heavy. In this paper, we propose a no-reference metric Q which is based upon singular value decomposition of local image gradient matrix, and provides a quantitative measure of true image content (i.e., sharpness and contrast as manifested in visually salient geometric features such as edges,) in the presence of noise and other disturbances. This measure 1) is easy to compute, 2) reacts reasonably to both blur and random noise, and 3) works well even when the noise is not Gaussian. The proposed measure is used to automatically and effectively set the parameters of two leading image denoising algorithms. Ample simulated and real data experiments support our claims. Furthermore, tests using the TID2008 database show that this measure correlates well with subjective quality evaluations for both blur and noise distortions.
Hole‐transporting materials (HTMs) play a significant role in hole transport and extraction for perovskite solar cells (PeSCs). As an important type of HTMs, the spiro‐architecture‐based material is ...widely used as small organic HTM in PeSCs with good photovoltaic performances. The skeletal modification of spiro‐based HTMs is a critical way of modifying energy level and hole mobility. Thus, many spiro alternatives are developed to optimize the spiro‐type HTMs. Herein, a novel carbazole‐based single‐spiro‐HTM named SCZF‐5 is designed and prepared for efficient PeSCs. In addition, another single‐spiro HTM SAF‐5 with reported 10‐phenyl‐10H‐spiroacridine‐9,9′‐fluorene (SAF) core is also synthesized for comparison. Through varying from SAF core to SCZF core as well as comparing with the classic 9,9′‐spiro‐bifluorene, it is found that the new HTM SCZF‐5 exhibits more impressive power conversion efficiency (PCE) of 20.10% than SAF‐5 (13.93%) and the commercial HTM spiro‐OMeTAD (19.11%). On the other hand, the SCZF‐5‐based device also has better durability in lifetime testing, indicating the newly designed SCZF by integrating carbazole into the spiro concept has good potential for developing effective HTMs.
Two novel spiro‐type hole‐transporting materials (HTMs) SCZF‐5 and SAF‐5 are designed based on different spiro‐cores, SZCF and SAF, respectively, and are applied in the perovskite solar cells. An impressive power conversion efficiency of 20.10% is achieved in the SCZF‐5‐based device, which is obviously higher than that of commercial HTM spiro‐OMeTAD (19.11%) and SAF‐5 (13.93%).
Genome-wide association studies (GWAS) aim to identify genetic factors associated with phenotypes. Standard analyses test variants for associations individually. However, variant-level associations ...are hard to identify and can be difficult to interpret biologically. Enrichment analyses help address both problems by targeting sets of biologically related variants. Here we introduce a new model-based enrichment method that requires only GWAS summary statistics. Applying this method to interrogate 4,026 gene sets in 31 human phenotypes identifies many previously-unreported enrichments, including enrichments of endochondral ossification pathway for height, NFAT-dependent transcription pathway for rheumatoid arthritis, brain-related genes for coronary artery disease, and liver-related genes for Alzheimer's disease. A key feature of our method is that inferred enrichments automatically help identify new trait-associated genes. For example, accounting for enrichment in lipid transport genes highlights association between MTTP and low-density lipoprotein levels, whereas conventional analyses of the same data found no significant variants near this gene.
Inspired by the cubic Mn4CaO5 cluster of natural oxygen‐evolving complex in Photosystem II, tetrametallic molecular water oxidation catalysts, especially M4O4 cubane‐like clusters (M=transition ...metals), have aroused great interest in developing highly active and robust catalysts for water oxidation. Among these M4O4 clusters, however, copper‐based molecular catalysts are poorly understood. Now, bio‐inspired Cu4O4 cubanes are presented as effective molecular catalysts for electrocatalytic water oxidation in aqueous solution (pH 12). The exceptional catalytic activity is manifested with a turnover frequency (TOF) of 267 s−1 for (LGly‐Cu)4 at 1.70 V and 105 s−1 for (LGlu‐Cu)4 at 1.56 V. Electrochemical and spectroscopic study revealed a successive two‐electron transfer process in the Cu4O4 cubanes to form high‐valent CuIII and CuIIIO. intermediates during the catalysis.
Cu4O4 cubanes show exceptional activity for electrocatalytic water oxidation in aqueous solution, with a turnover frequency of 267 s−1 for (LGly‐Cu)4 at 1.70 V and 105 s−1 for (LGlu‐Cu)4 at 1.56 V, respectively. The unique cuboidal structure and high catalytic performance is reminiscent of the natural Mn4CaO5 clusters in PS II.
To correct geometric distortion and reduce space and time-varying blur, a new approach is proposed in this paper capable of restoring a single high-quality image from a given image sequence distorted ...by atmospheric turbulence. This approach reduces the space and time-varying deblurring problem to a shift invariant one. It first registers each frame to suppress geometric deformation through B-spline-based nonrigid registration. Next, a temporal regression process is carried out to produce an image from the registered frames, which can be viewed as being convolved with a space invariant near-diffraction-limited blur. Finally, a blind deconvolution algorithm is implemented to deblur the fused image, generating a final output. Experiments using real data illustrate that this approach can effectively alleviate blur and distortions, recover details of the scene, and significantly improve visual quality.
Estimating the amount of blur in a given image is important for computer vision applications. More specifically, the spatially varying defocus point-spread-functions (PSFs) over an image reveal ...geometric information of the scene, and their estimate can also be used to recover an all-in-focus image. A PSF for a defocus blur can be specified by a single parameter indicating its scale. Most existing algorithms can only select an optimal blur from a finite set of candidate PSFs for each pixel. Some of those methods require a coded aperture filter inserted in the camera. In this paper, we present an algorithm estimating a defocus scale map from a single image, which is applicable to conventional cameras. This method is capable of measuring the probability of local defocus scale in the continuous domain. It also takes smoothness and color edge information into consideration to generate a coherent blur map indicating the amount of blur at each pixel. Simulated and real data experiments illustrate excellent performance and its successful applications in foreground/background segmentation.
Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs). However, their use of convolution operations with local receptive fields causes failures in ...modeling contextual spatial relations. Prior works have sought to address this issue by using graphical models or spatial propagation modules in networks. But such models often fail to capture long-range spatial relationships between entities, which leads to spatially fragmented predictions. Moreover, recent works have demonstrated that channel-wise information also acts a pivotal part in CNNs. In this article, we introduce two simple yet effective network units, the spatial relation module, and the channel relation module to learn and reason about global relationships between any two spatial positions or feature maps, and then produce Relation-Augmented (RA) feature representations. The spatial and channel relation modules are general and extensible, and can be used in a plug-and-play fashion with the existing fully convolutional network (FCN) framework. We evaluate relation module-equipped networks on semantic segmentation tasks using two aerial image data sets, namely International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam data sets, which fundamentally depend on long-range spatial relational reasoning. The networks achieve very competitive results, a mean <inline-formula> <tex-math notation="LaTeX">F_{1} </tex-math></inline-formula> score of 88.54% on the Vaihingen data set and a mean <inline-formula> <tex-math notation="LaTeX">F_{1} </tex-math></inline-formula> score of 88.01% on the Potsdam data set, bringing significant improvements over baselines.
Over the past few years, hyperspectral image classification using convolutional neural networks (CNNs) has progressed significantly. In spite of their effectiveness, given that hyperspectral images ...are of high dimensionality, CNNs can be hindered by their modeling of all spectral bands with the same weight, as probably not all bands are equally informative and predictive. Moreover, the usage of useless spectral bands in CNNs may even introduce noises and weaken the performance of networks. For the sake of boosting the representational capacity of CNNs for spectral-spatial hyperspectral data classification, in this work, we improve networks by discriminating the significance of different spectral bands. We design a network unit, which is termed as the spectral attention module, that makes use of a gating mechanism to adaptively recalibrate spectral bands by selectively emphasizing informative bands and suppressing less useful ones. We theoretically analyze and discuss why such a spectral attention module helps in a CNN for hyperspectral image classification. We demonstrate using extensive experiments that in comparison with state-of-the-art approaches, the spectral attention module-based convolutional networks are able to offer competitive results. Furthermore, this work sheds light on how a CNN interacts with spectral bands for the purpose of classification.
Object detection and semantic segmentation are two main themes in object retrieval from high-resolution remote sensing images, which have recently achieved remarkable performance by surfing the wave ...of deep learning and, more notably, convolutional neural networks. In this paper, we are interested in a novel, more challenging problem of vehicle instance segmentation, which entails identifying, at a pixel level, where the vehicles appear as well as associating each pixel with a physical instance of a vehicle. In contrast, vehicle detection and semantic segmentation each only concern one of the two. We propose to tackle this problem with a semantic boundary-aware multitask learning network. More specifically, we utilize the philosophy of residual learning to construct a fully convolutional network that is capable of harnessing multilevel contextual feature representations learned from different residual blocks. We theoretically analyze and discuss why residual networks can produce better probability maps for pixelwise segmentation tasks. Then, based on this network architecture, we propose a unified multitask learning network that can simultaneously learn two complementary tasks, namely, segmenting vehicle regions and detecting semantic boundaries. The latter subproblem is helpful for differentiating "touching" vehicles that are usually not correctly separated into instances. Currently, data sets with a pixelwise annotation for vehicle extraction are the ISPRS data set and the IEEE GRSS DFC2015 data set over Zeebrugge, which specializes in a semantic segmentation. Therefore, we built a new, more challenging data set for vehicle instance segmentation, called the Busy Parking Lot Unmanned Aerial Vehicle Video data set , and we make our data set available at http://www.sipeo.bgu.tum.de/downloads so that it can be used to benchmark future vehicle instance segmentation algorithms.
The rational construction of covalent or noncovalent organic two‐dimensional nanosheets is a fascinating target because of their promising applications in electronics, membrane technology, catalysis, ...sensing, and energy technologies. Herein, a large‐area (square millimeters) and free‐standing 2D supramolecular polymer (2DSP) single‐layer sheet (0.7–0.9 nm in thickness), comprising triphenylene‐fused nickel bis(dithiolene) complexes has been readily prepared by using the Langmuir–Blodgett method. Such 2DSPs exhibit excellent electrocatalytic activities for hydrogen generation from water with a Tafel slope of 80.5 mV decade−1 and an overpotential of 333 mV at 10 mA cm−2, which are superior to that of recently reported carbon nanotube supported molecular catalysts and heteroatom‐doped graphene catalysts. This work is promising for the development of novel free‐standing organic 2D materials for energy technologies.
Standing up: The Langmuir–Blodgett method can be used to prepare two‐dimensional supramolecular polymer (2DSP) sheets from nickel bis(dithiolene) complexes at the air–water interface (see figure). These free‐standing single‐layer sheets, which are 0.7–0.9 nm thick and square millimeters in area, showed excellent electrocatalytic activities in the hydrogen evolution reaction from water.
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