Deep learning-based channel estimation
In this letter, we present a deep learning algorithm for channel estimation in communication
systems. We consider the time-frequency response of a fast fading communication channel …
systems. We consider the time-frequency response of a fast fading communication channel …
A novel OFDM autoencoder featuring CNN-based channel estimation for internet of vessels
This article proposes a novel orthogonal frequency-division multiplexing (OFDM)
autoencoder featuring convolutional neural networks (CNNs)-based channel estimation for …
autoencoder featuring convolutional neural networks (CNNs)-based channel estimation for …
Image super-resolution via dual-dictionary learning and sparse representation
Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from
the prior model trained by a set of high-and low-resolution image patches. In this paper, HF …
the prior model trained by a set of high-and low-resolution image patches. In this paper, HF …
Image restoration using very deep convolutional encoder-decoder networks with symmetric skip connections
In this paper, we propose a very deep fully convolutional encoding-decoding framework for
image restoration such as denoising and super-resolution. The network is composed of …
image restoration such as denoising and super-resolution. The network is composed of …
Super-resolution via image-adapted denoising CNNs: Incorporating external and internal learning
While deep neural networks exhibit state-of-the-art results in the task of image super-
resolution (SR) with a fixed known acquisition process (eg, a bicubic downscaling kernel) …
resolution (SR) with a fixed known acquisition process (eg, a bicubic downscaling kernel) …
New learning based super-resolution: use of DWT and IGMRF prior
PP Gajjar, MV Joshi - IEEE Transactions on Image Processing, 2010 - ieeexplore.ieee.org
In this paper, we propose a new learning-based approach for super-resolving an image
captured at low spatial resolution. Given the low spatial resolution test image and a …
captured at low spatial resolution. Given the low spatial resolution test image and a …
Achieving super-resolution remote sensing images via the wavelet transform combined with the recursive res-net
W Ma, Z Pan, J Guo, B Lei - IEEE Transactions on Geoscience …, 2019 - ieeexplore.ieee.org
Deep learning (DL) has been successfully applied to single image super-resolution (SISR),
which aims at reconstructing a high-resolution (HR) image from its low-resolution (LR) …
which aims at reconstructing a high-resolution (HR) image from its low-resolution (LR) …
Learning deconvolutional deep neural network for high resolution medical image reconstruction
Super resolution reconstruction can be used to recover a high resolution image from a low
resolution image and is particularly beneficial for clinically significant medical images in …
resolution image and is particularly beneficial for clinically significant medical images in …
Initial results on deep learning for joint channel equalization and decoding
Historically, most of the channel encoding and decoding algorithms have been designed to
deal with and evaluated under the additive white Gaussian noise (AWGN) channel …
deal with and evaluated under the additive white Gaussian noise (AWGN) channel …
Stochastic frequency masking to improve super-resolution and denoising networks
Super-resolution and denoising are ill-posed yet fundamental image restoration tasks. In
blind settings, the degradation kernel or the noise level are unknown. This makes restoration …
blind settings, the degradation kernel or the noise level are unknown. This makes restoration …