In recent years, with the rapid development of deep learning, super-resolution methods
based on convolutional neural networks (CNNs) have made great progress. However, the …

In this paper, we tackle the problem of single image dehazing with a convolutional neural
network. Within this network, we develop a multi-level fusion module to utilize both low-level …

Convolutional neural networks have been proven to be of great benefit for single-image
super-resolution (SISR). However, previous works do not make full use of multi-scale …

Infrared image retains typical thermal targets while visible image preserves rich texture
details, image fusion aims to reconstruct a synthesized image containing prominent targets …

Deep convolutional neural networks (DCNNs) have exhibited excellent feature extraction
and detail reconstruction capabilities for single image super-resolution (SISR) …

Recently, deep convolutional neural networks (CNNs) have been widely explored in single
image super-resolution (SISR) and contribute remarkable progress. However, most of the …

The effective utilization of hyperspectral image (HSI) and light detection and ranging
(LiDAR) data is essential for land cover classification. Recently, deep learning-based …

针对传统卷积神经网络在人脸表情识别过程中存在有效特征提取针对性不强,
识别准确率不高的问题, 提出一种基于多尺度特征注意力机制的人脸表情识别方法 …

Abstract Single Image Super-Resolution (SISR) is a complex restoration method to recover
high-resolution (HR) image from degraded low-resolution (LR) form. SISR is used in many …

Recently, the attention mechanism became the key issue for image super-resolution (SR)
because it has the ability to extract different features from the image according to the used …