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18 pages, 4338 KiB

Open AccessArticle

Infrared and Visible Image Fusion with Significant Target Enhancement

by Xing Huo, Yinping Deng and Kun Shao

Entropy 2022, 24(11), 1633; https://doi.org/10.3390/e24111633 - 10 Nov 2022

Cited by 6 | Viewed by 2644

Existing fusion rules focus on retaining detailed information in the source image, but as the thermal radiation information in infrared images is mainly characterized by pixel intensity, these fusion rules are likely to result in reduced saliency of the target in the fused image. To address this problem, we propose an infrared and visible image fusion model based on significant target enhancement, aiming to inject thermal targets from infrared images into visible images to enhance target saliency while retaining important details in visible images. First, the source image is decomposed with multi-level Gaussian curvature filtering to obtain background information with high spatial resolution. Second, the large-scale layers are fused using ResNet50 and maximizing weights based on the average operator to improve detail retention. Finally, the base layers are fused by incorporating a new salient target detection method. The subjective and objective experimental results on TNO and MSRS datasets demonstrate that our method achieves better results compared to other traditional and deep learning-based methods. Full article

(This article belongs to the Special Issue Advances in Image Fusion)

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28 pages, 890 KiB

Open AccessArticle

Modelling USA Age-Cohort Mortality: A Comparison of Multi-Factor Affine Mortality Models

by Zhiping Huang, Michael Sherris, Andrés M. Villegas and Jonathan Ziveyi

Risks 2022, 10(9), 183; https://doi.org/10.3390/risks10090183 - 15 Sep 2022

Cited by 4 | Viewed by 2775

Abstract

Affine mortality models are well suited for theoretical and practical application in pricing and risk management of mortality risk. They produce consistent, closed-form stochastic survival curves allowing for the efficient valuation of mortality-linked claims. We model USA age-cohort mortality data using five multi-factor affine mortality models. We focus on three-factor models and compare four Gaussian models along with a model based on the Cox–Ingersoll–Ross (CIR) process, allowing for Gamma-distributed mortality rates. We compare and assess the Gaussian Arbitrage-Free Nelson–Siegel (AFNS) mortality model, which incorporates level, slope and curvature factors, and the canonical Gaussian factor model, both with and without correlations in the factor dynamics. We show that for USA mortality data, the probability of negative mortality rates in the Gaussian models is small. Models are estimated using discrete time versions of the models with age-cohort data capturing variability in cohort mortality curves. Poisson variation in mortality data is included in the model estimation using the Kalman filter through the measurement equation. We consider models incorporating factor dependence to capture the effects of age-dependence in the mortality curves. The analysis demonstrates that the Gaussian independent-factor AFNS model performs well compared to the other affine models in explaining and forecasting USA age-cohort mortality data. Full article

(This article belongs to the Special Issue Longevity Risk Modelling and Management)

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