To address these issues, in this article, we propose a dense lightweight network, called MADNet, for stronger multiscale feature expression and feature correlation learning. Specifically, a residual multiscale module with an attention mechanism (RMAM) is developed to enhance the informative multiscale feature representation ability.

2020年3月4日 · To address these issues, in this article, we propose a dense lightweight network, called MADNet, for stronger multiscale feature expression and feature correlation learning.

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Architecture of our proposed model (MADNet), which contains two subnetworks: an EFEN and a UN. The former includes three DRPBs; the latter is constructed by three sets of Conv layers and a pixel...

Therefore, we address this side effect by introducing a new lightweight, yet effective, deep stereo architecture Modularly ADaptive Network (MADNet) and by developing Modular ADaptation (MAD), an algorithm to train independently only sub-portions of our model.

These elaborate strategies form a novel network for satellite VSR, termed MADNet, which achieves favorable performance against state-of-the-art method BasicVSR++ in terms of average PSNR by 0.14 dB on various video satellites, including JiLin-1, …

MADNET: a fast and lightweight network designed specifically for single-image super-resolution. MADNET leverages innovative architectural design and efficient computational strategies to achieve high-quality image enhancement while minimizing computational overhead and memory usage. The core of MADNET is

MADnet: a Multiple Attention Decoder Network for Segmentation of Remote Sensing Images Abstract: Segmentation of high-resolution remote sensing images is a challenging task. Some recent studies have used complex convolutional neural networks to address this problem, and they can only extract multi-scale features and improve object boundary ...

2021年10月21日 · In this paper, we improve upon a previously developed single-image DTM estimation system called MADNet (1.0). We propose optimisations which we collectively call MADNet 2.0, which is based on a supervised image-to-height estimation network, multi-scale DTM reconstruction, and 3D co-alignment processes.

In addition, most CNN-based methods rarely explore the intermediate features that are helpful for final image recovery. To address these issues, in this article, we propose a dense lightweight network, called MADNet, for stronger multiscale feature expression and …