416 research outputs found
Deep Networks for Image Super-Resolution with Sparse Prior
Deep learning techniques have been successfully applied in many areas of
computer vision, including low-level image restoration problems. For image
super-resolution, several models based on deep neural networks have been
recently proposed and attained superior performance that overshadows all
previous handcrafted models. The question then arises whether large-capacity
and data-driven models have become the dominant solution to the ill-posed
super-resolution problem. In this paper, we argue that domain expertise
represented by the conventional sparse coding model is still valuable, and it
can be combined with the key ingredients of deep learning to achieve further
improved results. We show that a sparse coding model particularly designed for
super-resolution can be incarnated as a neural network, and trained in a
cascaded structure from end to end. The interpretation of the network based on
sparse coding leads to much more efficient and effective training, as well as a
reduced model size. Our model is evaluated on a wide range of images, and shows
clear advantage over existing state-of-the-art methods in terms of both
restoration accuracy and human subjective quality
Fractal analysis of the effect of particle aggregation distribution on thermal conductivity of nanofluids
This project was supported by the National Natural Science Foundation of China (No. 41572116), the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan) (No. CUG160602).Peer reviewedPostprin
Matching-CNN Meets KNN: Quasi-Parametric Human Parsing
Both parametric and non-parametric approaches have demonstrated encouraging
performances in the human parsing task, namely segmenting a human image into
several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim
to develop a new solution with the advantages of both methodologies, namely
supervision from annotated data and the flexibility to use newly annotated
(possibly uncommon) images, and present a quasi-parametric human parsing model.
Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the
parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict
the matching confidence and displacements of the best matched region in the
testing image for a particular semantic region in one KNN image. Given a
testing image, we first retrieve its KNN images from the
annotated/manually-parsed human image corpus. Then each semantic region in each
KNN image is matched with confidence to the testing image using M-CNN, and the
matched regions from all KNN images are further fused, followed by a superpixel
smoothing procedure to obtain the ultimate human parsing result. The M-CNN
differs from the classic CNN in that the tailored cross image matching filters
are introduced to characterize the matching between the testing image and the
semantic region of a KNN image. The cross image matching filters are defined at
different convolutional layers, each aiming to capture a particular range of
displacements. Comprehensive evaluations over a large dataset with 7,700
annotated human images well demonstrate the significant performance gain from
the quasi-parametric model over the state-of-the-arts, for the human parsing
task.Comment: This manuscript is the accepted version for CVPR 201
Unveiling the Roles of Binder in the Mechanical Integrity of Electrodes for Lithium-Ion Batteries
In lithium-ion secondary batteries research, binders have received the least attention, although the electrochemical performance of Li-ion batteries such as specific capacity and cycle life cannot be achieved if the adhesion strengths between electrode particles and between electrode films and current collectors are insufficient to endure charge-discharge cycling. In this paper, the roles of binders in the mechanical integrity of electrodes for lithium-ion batteries were studied by coupled microscratch and digital image correlation (DIC) techniques. A microscratch based composite model was developed to decouple the carbon particle/particle cohesion strength from the electrode-film/copper-current-collector adhesion strength. The dependences of microscratch coefficient of friction and the critical delamination load on the PVDF binder content suggest that the strength of different interfaces is ranked as follows: Cu/PVDF \u3c carbon-particle/PVDF \u3c PVDF/PVDF. The particle/particle cohesion strength increases while electrode-film/current-collector adhesion strength decreases with increasing PVDF binder content (up to 20% of binder). The electrolyte soaking-and-drying process leads to an increase in particle/particle cohesion but a decrease in electrode-film/copper-current-collector adhesion. Finally, the methodology developed here can provide new guidelines for binder selection and electrode design and lay a constitutive foundation for modeling the mechanical properties and performance of the porous electrodes in lithium-ion batteries
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