多媒体系统(MS)期刊详细介绍了多媒体计算,通信,存储和应用的各个方面的创新研究思想,新兴技术,最新方法和工具。它包含理论,实验和调查文章。多媒体系统的覆盖范围包括:在计算机系统中集成数字视频和音频功能;多媒体信息编码和数据交换格式;数字多媒体的操作系统机制;数字视频和音频网络与通信;存储模型和结构;用于支持多媒体应用程序的方法、范式、工具和软件体系结构;多媒体应用程序和应用程序接口,以及多媒体终端系统架构。 官网地址:http://dblp.uni-trier.de/db/journals/mms/

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Pansharpening in remote sensing image aims at acquiring a high-resolution multispectral (HRMS) image directly by fusing a low-resolution multispectral (LRMS) image with a panchromatic (PAN) image. The main concern is how to effectively combine the rich spectral information of LRMS image with the abundant spatial information of PAN image. Recently, many methods based on deep learning have been proposed for the pansharpening task. However, these methods usually has two main drawbacks: 1) requiring HRMS for supervised learning; and 2) simply ignoring the latent relation between the MS and PAN image and fusing them directly. To solve these problems, we propose a novel unsupervised network based on learnable degradation processes, dubbed as LDP-Net. A reblurring block and a graying block are designed to learn the corresponding degradation processes, respectively. In addition, a novel hybrid loss function is proposed to constrain both spatial and spectral consistency between the pansharpened image and the PAN and LRMS images at different resolutions. Experiments on Worldview2 and Worldview3 images demonstrate that our proposed LDP-Net can fuse PAN and LRMS images effectively without the help of HRMS samples, achieving promising performance in terms of both qualitative visual effects and quantitative metrics.

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