Deblurring Videos Using Spatial-Temporal Contextual Transformer With Feature Propagation.

We present a simple and effective approach to explore both local spatial-temporal contexts and non-local temporal information for video deblurring. First, we develop an effective spatial-temporal contextual transformer to explore local spatial-temporal contexts from videos. As the features extracted by the spatial-temporal contextual transformer does not model the non-local temporal information of video well, we then develop a feature propagation method to aggregate useful features from the long-range frames so that both local spatial-temporal contexts and non-local temporal information can be better utilized for video deblurring.

Deep self-supervised spatial-variant image deblurring.

Most existing model-based and learning-based image deblurring methods usually use synthetic blur-sharp training pairs to remove blur. However, these approaches do not perform well in real-world applications as the blur-sharp training pairs are difficult to be obtained and the blur in real-world scenarios is spatial-variant. In this paper, we propose a self-supervised learning-based image deblurring method that can deal with both uniform and spatial-variant blur distributions.

BiSTNet: Semantic Image Prior Guided Bidirectional Temporal Feature Fusion for Deep Exemplar-Based Video Colorization.

How to effectively explore the colors of exemplars and propagate them to colorize each frame is vital for exemplar-based video colorization. In this article, we present a BiSTNet to explore colors of exemplars and utilize them to help video colorization by a bidirectional temporal feature fusion with the guidance of semantic image prior. We first establish the semantic correspondence between each frame and the exemplars in deep feature space to explore color information from exemplars.

Self-Supervised Deep Blind Video Super-Resolution.

Existing deep learning-based video super-resolution (SR) methods usually depend on the supervised learning approach, where the training data is usually generated by the blurring operation with known or predefined kernels (e.g., Bicubic kernel) followed by a decimation operation.

The Oxygen Evolution Reaction Drives Passivity Breakdown for Ni-Cr-Mo Alloys.

Corrosion is the main factor limiting the lifetime of metallic materials, and a fundamental understanding of the governing mechanism and surface processes is difficult to achieve since the thin oxide films at the metal-liquid interface governing passivity are notoriously challenging to study. In this work, a combination of synchrotron-based techniques and electrochemical methods is used to investigate the passive film breakdown of a Ni-Cr-Mo alloy, which is used in many industrial applications. This alloy is found to be active toward oxygen evolution reaction (OER), and the OER onset coincides with the loss of passivity and severe metal dissolution.

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-Local Spatial-Temporal Similarity.

We present compact and effective deep convolutional neural networks (CNNs) by exploring properties of videos for video deblurring. Motivated by the non-uniform blur property that not all the pixels of the frames are blurry, we develop a CNN to integrate a temporal sharpness prior (TSP) for removing blur in videos. The TSP exploits sharp pixels from adjacent frames to facilitate the CNN for better frame restoration.

Bi-Directional Pseudo-Three-Dimensional Network for Video Frame Interpolation.

Recent video frame interpolation methods have employed the curvilinear motion model to accommodate nonlinear motion among frames. The effectiveness of such model often hinges on motion estimation and occlusion detection, and therefore is greatly challenged when these methods are used to handle dynamic scenes that contain complex motions and occlusions. We address the challenges by proposing a bi-directional pseudo-three-dimensional network to exploit the correlation between motion estimation and depth-related occlusion estimation that considers the third dimension: depth.

Self-Powered, Long-Durable, and Highly Selective Oil-Solid Triboelectric Nanogenerator for Energy Harvesting and Intelligent Monitoring.

Triboelectric nanogenerators (TENGs) have potential to achieve energy harvesting and condition monitoring of oils, the "lifeblood" of industry. However, oil absorption on the solid surfaces is a great challenge for oil-solid TENG (O-TENG). Here, oleophobic/superamphiphobic O-TENGs are achieved via engineering of solid surface wetting properties.

Deep Ranking Exemplar-Based Dynamic Scene Deblurring.

Dynamic scene deblurring is a challenging problem as it is difficult to be modeled mathematically. Benefiting from the deep convolutional neural networks, this problem has been significantly advanced by the end-to-end network architectures. However, the success of these methods is mainly due to simply stacking network layers.

Self-Guided Image Dehazing Using Progressive Feature Fusion.

We propose an effective image dehazing algorithm which explores useful information from the input hazy image itself as the guidance for the haze removal. The proposed algorithm first uses a deep pre-dehazer to generate an intermediate result, and takes it as the reference image due to the clear structures it contains. To better explore the guidance information in the generated reference image, it then develops a progressive feature fusion module to fuse the features of the hazy image and the reference image.

Learning Discriminative Cross-Modality Features for RGB-D Saliency Detection.

How to explore useful information from depth is the key success of the RGB-D saliency detection methods. While the RGB and depth images are from different domains, a modality gap will lead to unsatisfactory results for simple feature concatenation. Towards better performance, most methods focus on bridging this gap and designing different cross-modal fusion modules for features, while ignoring explicitly extracting some useful consistent information from them.

Learning Spatially Variant Linear Representation Models for Joint Filtering.

Joint filtering mainly uses an additional guidance image as a prior and transfers its structures to the target image in the filtering process. Different from existing approaches that rely on local linear models or hand-designed objective functions to extract the structural information from the guidance image, we propose a new joint filtering method based on a spatially variant linear representation model (SVLRM), where the target image is linearly represented by the guidance image. However, learning SVLRMs for vision tasks is a highly ill-posed problem.

Real-Time and Online Lubricating Oil Condition Monitoring Enabled by Triboelectric Nanogenerator.

An intelligent monitoring lubricant is essential for the development of smart machines because unexpected and fatal failures of critical dynamic components in the machines happen every day, threatening the life and health of humans. Inspired by the triboelectric nanogenerators (TENGs) work on water, we present a feasible way to prepare a self-powered triboelectric sensor for real-time monitoring of lubricating oils the contact electrification process of oil-solid contact (O-S TENG). Typical intruding contaminants in pure base oils can be successfully monitored.

Multi-Stage Degradation Homogenization for Super-Resolution of Face Images With Extreme Degradations.

Face Super-Resolution (FSR) aims to infer High-Resolution (HR) face images from the captured Low-Resolution (LR) face image with the assistance of external information. Existing FSR methods are less effective for the LR face images captured with serious low-quality since the huge imaging/degradation gap caused by the different imaging scenarios (i.e.

Deblurring Dynamic Scenes via Spatially Varying Recurrent Neural Networks.

Deblurring images captured in dynamic scenes is challenging as the motion blurs are spatially varying caused by camera shakes and object movements. In this paper, we propose a spatially varying neural network to deblur dynamic scenes. The proposed model is composed of three deep convolutional neural networks (CNNs) and a recurrent neural network (RNN).

Deep Outlier Handling for Image Deblurring.

Outlier handling has attracted considerable attention recently but remains challenging for image deblurring. Existing approaches mainly depend on iterative outlier detection steps to explicitly or implicitly reduce the influence of outliers on image deblurring. However, these outlier detection steps usually involve heuristic operations and iterative optimization processes, which are complex and time-consuming.

Robust Face Super-Resolution via Position Relation Model Based on Global Face Context.

Because Face Super-Resolution (FSR) tends to infer High-Resolution (HR) face image by breaking the given Low- Resolution (LR) image into individual patches and inferring the HR correspondence one patch by one separately, Super- Resolution (SR) of face images with serious degradation, especially with occlusion, is still a challenging problem of the computer vision field. To address this problem, we propose a patch-level face model for FSR, which we called the position relation model. This model consists of the mapping relationships in every face position to the rest of the face positions based on similarity.

Deep Video Deblurring Using Sharpness Features from Exemplars.

Video deblurring is a challenging problem as the blur in videos is usually caused by camera shake, object motion, depth variation, etc. Existing methods usually impose handcrafted image priors or use end-to-end trainable networks to solve this problem. However, using image priors usually leads to highly non-convex problems while directly using end-to-end trainable networks in a regression generates over-smoothes details in the restored images.

Task-Oriented Network for Image Dehazing.

Haze interferes the transmission of scene radiation and significantly degrades color and details of outdoor images. Existing deep neural networks-based image dehazing algorithms usually use some common networks. The network design does not model the image formation of haze process well, which accordingly leads to dehazed images containing artifacts and haze residuals in some special scenes.

Physics-Based Generative Adversarial Models for Image Restoration and Beyond.

We present an algorithm to directly solve numerous image restoration problems (e.g., image deblurring, image dehazing, and image deraining).

Co-Adsorption of HO, OH, and Cl on Aluminum and Intermetallic Surfaces and Its Effects on the Work Function Studied by DFT Calculations.

The energetics of adsorption of HO layers and HO layers partially replaced with OH or Cl on an Al(111) surface and on selected surfaces of intermetallic phases, MgSi and AlCu, was studied by first-principle calculations using the density function theory (DFT). The results show that HO molecules tended to bind to all investigated surfaces with an adsorption energy in a relatively narrow range, between -0.8 eV and -0.

Mussel-Inspired Graphene Film with Enhanced Durability as a Macroscale Solid Lubricant.

Graphene has exhibited massive potential as a macroscale solid lubricant, but its durability is limited due to the weak adhesion between graphene sheets and the substrate. Here, inspired by mussel adhesive protein (MAP), effective reinforcement of the graphene-substrate interaction to attain remarkable enhancement on the durability of the graphene film is presented. The mussel-inspired graphene (mGr) film exhibits a coefficient of friction stabilizing at 0.