289 research outputs found
Multi-scale Analysis based Image Fusion
Image fusion provides a better view than that provided by any of the individual source images. The aim of multi-scale analysis is to find a kind of optimal representation for high dimensional information expression. Based on the nonlinear approximation, the principle and ways of image fusion are studied, and its development, current and future challenges are reviewed in this paper.The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, Japa
Multi-scale Analysis based Image Fusion
The 2nd International Conference on Intelligent Systems and Image Processing 2014 (ICISIP2014), September 26-29, 2014, Nishinippon Institute of Technology, Kitakyushu, JapanImage fusion provides a better view than that provided by any of the individual source images. The aim of multi-scale analysis is to find a kind of optimal representation for high dimensional information expression. Based on the nonlinear approximation, the principle and ways of image fusion are studied, and its development, current and future challenges are reviewed in this paper
Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution
Existing real-world video super-resolution (VSR) methods focus on designing a
general degradation pipeline for open-domain videos while ignoring data
intrinsic characteristics which strongly limit their performance when applying
to some specific domains (eg., animation videos). In this paper, we thoroughly
explore the characteristics of animation videos and leverage the rich priors in
real-world animation data for a more practical animation VSR model. In
particular, we propose a multi-scale Vector-Quantized Degradation model for
animation video Super-Resolution (VQD-SR) to decompose the local details from
global structures and transfer the degradation priors in real-world animation
videos to a learned vector-quantized codebook for degradation modeling. A
rich-content Real Animation Low-quality (RAL) video dataset is collected for
extracting the priors. We further propose a data enhancement strategy for
high-resolution (HR) training videos based on our observation that existing HR
videos are mostly collected from the Web which contains conspicuous compression
artifacts. The proposed strategy is valid to lift the upper bound of animation
VSR performance, regardless of the specific VSR model. Experimental results
demonstrate the superiority of the proposed VQD-SR over state-of-the-art
methods, through extensive quantitative and qualitative evaluations of the
latest animation video super-resolution benchmark. The code and pre-trained
models can be downloaded at https://github.com/researchmm/VQD-SR
Multiple solutions for a kind of periodic boundary value problems via variation approach
In this paper, the existence of at least two solutions for periodic boundary value problems is obtained by the critical point theory. The interest is that the nonlinear term includes the first-order derivative and may not satisfy the classical Ambrosetti-Rabinowitz condition
Reliability Estimation of Reciprocating Seals Based on Multivariate Dependence Analysis and It\u27s Experimental Validation
Accurate reliability estimation for reciprocating seals is of great significance due to their wide use in numerous engineering applications. This work proposes a reliability estimation method for reciprocating seals based on multivariate dependence analysis of different performance indicators. Degradation behavior corresponding to each performance indicator is first described by the Wiener process. Dependence among different performance indicators is then captured using D-vine copula, and a weight-based copula selection method is utilized to determine the optimal bivariate copula for each dependence relationship. A two-stage Bayesian method is used to estimate the parameters in the proposed model. Finally, a reciprocating seal degradation test is conducted, and the proposed reliability estimation approach is validated by test data. Results show that the proposed model is accurate and effective in estimating the reliability of reciprocating seals
Predictive Chemistry Augmented with Text Retrieval
This paper focuses on using natural language descriptions to enhance
predictive models in the chemistry field. Conventionally, chemoinformatics
models are trained with extensive structured data manually extracted from the
literature. In this paper, we introduce TextReact, a novel method that directly
augments predictive chemistry with texts retrieved from the literature.
TextReact retrieves text descriptions relevant for a given chemical reaction,
and then aligns them with the molecular representation of the reaction. This
alignment is enhanced via an auxiliary masked LM objective incorporated in the
predictor training. We empirically validate the framework on two chemistry
tasks: reaction condition recommendation and one-step retrosynthesis. By
leveraging text retrieval, TextReact significantly outperforms state-of-the-art
chemoinformatics models trained solely on molecular data.Comment: EMNLP 202
Assessing PM(sub 2.5) Exposures with High Spatiotemporal Resolution Across the Continental United States
A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index and meteoroidal fields, are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed good model performance with total R2 of 0.84 on the left out monitors. Regional R2 could be even higher for the Eastern and Central United States. Model performance was still good at low PM2.5 concentrations. Then, we used the trained neural network to make daily prediction of PM2.5 at 1 km 1 km grid cells. This model allows epidemiologists to access PM2.5 exposure in both the short term and the long term
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