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您的检索式：作者名="Renwei Dian"
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| 1 | PAPS: Progressive Attention-Based Pan-sharpening显示文摘Pan-sharpening aims to seek high-resolution multispectral(HRMS) images from paired multispectral images of low resolution(LRMS) and panchromatic(PAN) images, the key to which is how to maximally integrate spatial and spectral information from PAN and LRMS images. Following the principle of gradual advance, this paper designs a novel network that contains two main logical functions, i.e., detail enhancement and progressive fusion, to solve the problem. More specifically, the detail enhancement module attempts to produce enhanced MS results with the same spatial sizes as corresponding PAN images, which are of higher quality than directly up-sampling LRMS images.Having a better MS base(enhanced MS) and its PAN, we progressively extract information from the PAN and enhanced MS images, expecting to capture pivotal and complementary information of the two modalities for the purpose of constructing the desired HRMS. Extensive experiments together with ablation studies on widely-used datasets are provided to verify the efficacy of our design, and demonstrate its superiority over other state-of-the-art methods both quantitatively and qualitatively. Our code has been released at http://gffzz188fe103f8f1460asqb90pv6x6bp56kkp.ffgz.tsg.suse.edu.cn/JiaYN1/PAPS. | Yanan Jia Qiming Hu Renwei Dian Jiayi Ma Xiaojie Guo | 2024 | IEEE/CAA Journal of Automatica Sinica2024，11，2： | 0 |
| 2 | Learning the external and internal priors for multispectral and hyperspectral image fusion显示文摘Recently,multispectral image(MSI)and hyperspectral image(HSI)fusion has been a popular topic in high-resolution HSI acquisition.This fusion leads to a challenging underdetermined problem,which image priors are used to regularize,aiming at improving fusion accuracy.To fully exploit HSI priors,this paper proposes two kinds of priors,i.e.,external priors and internal priors,to regularize the fusion problem.An external prior represents the general image characteristics and is learned from abundant training data by using a Gaussian denoising convolutional neural network(CNN)trained in the additional gray images.An internal prior represents the unique characteristics of the HSI and MSI to be fused.To learn the external prior,we first segment the MSI into several superpixels and then enforce a low-rank constraint for each superpixel,which can well model local similarities in the HSI.In addition,to model a low-rank property in the spectral mode,the high-resolution HSI is decomposed into a low-rank spectral basis and abundances.Finally,we formulate the fusion as an external and internal prior-regularized optimization problem,which is efficiently tackled through the alternating direction method of multipliers.Experiments on simulated and real datasets demonstrate the superiority of the proposed method. | Shutao LI Renwei DIAN Haibo LIU | 2023 | Science China(Information Sciences)2023，66，4： | 0 |
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