Convolutional neural networks have made a great breakthrough in recent remote sensing
image super-resolution (SR) tasks. Most of these methods adopt upsampling layers at the …

The past few years have seen an accelerating integration of deep learning (DL) techniques
into various remote sensing (RS) applications, highlighting their power to adapt and …

Recently, deep convolutional neural networks (CNNs) have made great progress in remote
sensing image super-resolution (SR). The CNN-based methods can learn powerful feature …

Current super-resolution networks typically reduce network parameters and multiadds
operations by designing lightweight structures, but lightening the convolution layer is often …

Remote Sensing (RS) images are usually captured at resolutions lower than those required.
Deep Learning (DL)-based super-resolution (SR) architectures are typically used to …

High-resolution (HR) remote sensing imagery plays a critical role in remote sensing image
interpretation, and single image super-resolution (SISR) reconstruction technology is …

Despite its fruitful applications in remote sensing, image super-resolution (SR) is
troublesome to train and deploy as it handles different resolution magnifications with …

Recently, remote sensing image super-resolution (RSISR) has drawn considerable attention
and made great breakthroughs based on convolutional neural networks (CNNs). Due to the …

This paper proposes SquiggleMilli, a system that approximates traditional Synthetic Aperture
Radar (SAR) imaging on mobile millimeter-wave (mmWave) devices. The system is capable …

The pursuit of superresolution (SR) with large upscaling factors, such as, for enhancing the
spatial resolution of low-resolution (LR) remote sensing images is a persistent and …