Single image super-resolution (SISR) has witnessed great strides with the development of
deep learning. However, most existing studies focus on building more complex networks …

The performance of single image super-resolution has achieved significant improvement by
utilizing deep convolutional neural networks (CNNs). The features in deep CNN contain …

In this paper, we propose a deep-learning-based infrared-visible images fusion method
based on encoder-decoder architecture. The image fusion task is reformulated as a problem …

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 …

Image super-resolution technology successfully overcomes the limitation of excessively
large pixel size in infrared detectors and meets the increasing demand for high-resolution …

Single image super-resolution (SR) is an established pixel-level vision task aimed at
reconstructing a high-resolution image from its degraded low-resolution counterpart. Despite …

Image Super-Resolution (SR) is essential for a wide range of computer vision and image
processing tasks. Investigating infrared (IR) image (or thermal images) super-resolution is a …

Single image super-resolution (SISR) is an active research topic in the fields of image
processing, computer vision and pattern recognition, restoring high-frequency details and …

Deep Convolutional Neural Networks (CNN) have drawn great attention in image super-
resolution (SR). Recently, visual attention mechanism, which exploits both of the feature …

Two-dimensional (2D) materials have garnered considerable attention due to their
advantageous properties, including tunable bandgap, prominent carrier mobility, tunable …