Remote sensing image fusion aims at fusing high spatial resolution single-band
panchromatic (PAN) image with spectrally informative multispectral (MS) image to generate …

Whether conventional machine learning-based or current deep neural networks-based
single image super-resolution (SISR) methods, they are generally trained and validated on …

Stereo image super-resolution aims to exploit the complementary information between
image pairs and generate images with high resolution and rich details. However, existing …

Owing to the limitations of onboard equipment, the direct acquisition of high-resolution
images in unmanned aerial vehicle (UAV) target observation tasks is a challenge. However …

Convolution-based networks show impressive performance in Single Image Super-
Resolution (SISR) task by making full use of local patterns. However, it requires deeper …

To explore a fast and accurate image superresolution (SR) algorithm for real-world
scenarios, information distillation is used to compress the number of feature channels, which …

Multi-modal medical image super-resolution (SR) plays a vital role in enhancing the
resolution of medical images, providing more detailed visuals that aid in accurate clinical …

Although recent video super-resolution (VSR) works show remarkable restoration
performance for low-resolution (LR) video downscaled by a fixed known blur kernel, blind …

Deep and wide Convolutional neural networks (CNNs) achieve remarkable performance on
image super-resolution (SR), while it is hard to be directly applied on edge devices due to …

Adverse weather removal aims to recover clean images from environments affected by
adverse weather conditions. However, few attempts have focused on addressing the …