Recent efforts have explored leveraging visible light images to enrich texture details in
infrared (IR) super-resolution. However, this direct adaptation approach often becomes a …

The infrared image super-resolution (SR) method successfully overcomes the hardware
limitations of infrared cameras that reconstruct higher-quality images with improved …

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 …

Infrared imaging has broad and important applications. However, the infrared detector
manufacture technique limits the detector resolution and the resolution of infrared images. In …

The breakthrough of sensor resolution has always been focused on the computer vision
area, which is an urgent need when infrared sensor technology progresses slowly. We …

Given the broad application of infrared technology across diverse fields, there is an
increasing emphasis on investigating super-resolution techniques for infrared images within …

Most existing convolution neural network (CNN) based super-resolution (SR) methods
generate their paired training dataset by artificially synthesizing low-resolution (LR) images …

Due to the limitation of hardware, infrared (IR) images have low-resolution (LR) and poor
visual quality. Image super-resolution (SR) is a good solution to this problem. In this paper …

Deep convolutional neural networks are capable of achieving remarkable performance in
single-image super-resolution (SISR). However, due to the weak availability of infrared …

Transformer-based methods have exhibited remarkable potential in single image super-
resolution (SISR) by effectively extracting long-range dependencies. However, most of the …