ConvNets can compete with transformers in high-level tasks by exploiting larger receptive
fields. To unleash the potential of ConvNet in super-resolution we propose a multi-scale …

Advanced diffusion models (DMs) perform impressively in image super-resolution (SR), but
the high memory and computational costs hinder their deployment. Binarization, an ultra …

With the emergence of mobile devices, there is a growing demand for an efficient model to
restore any degraded image for better perceptual quality. However, existing models often …

Image super-resolution methods have made significant strides with deep learning
techniques and ample training data. However, they face challenges due to inherent …

It is a difficult task to achieve an efficient balance between Single Image Super Resolution
(SISR) model complexity and performance, since the model performance improvement is not …

Recent advancements in all-in-one image restoration models have revolutionized the ability
to address diverse degradations through a unified framework. However, parameters tied to …

Lightweight networks for single image super-resolution (SISR) have increased in popularity,
and numerous lightweight SISR networks have been proposed for various practical …

Existing underwater image restoration (UIR) methods generally only handle color distortion
or jointly address color and haze issues, but they often overlook the more complex …

Lightweight image super-resolution (SR) methods aim at increasing the resolution and
restoring the details of an image using a lightweight neural network. However, current …