Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

Single image super-resolution (SISR), which aims to reconstruct a high-resolution (HR)
image from a low-resolution (LR) observation, has been an active research topic in the area …

Over the past few years, single image super-resolution (SR) has become a hotspot in the
remote sensing area, and numerous methods have made remarkable progress in this …

Existing image restoration methods mostly leverage the posterior distribution of natural
images. However, they often assume known degradation and also require supervised …

Test-time training adapts to a new test distribution on the fly by optimizing a model for each
test input using self-supervision. In this paper, we use masked autoencoders for this one …

It is widely acknowledged that single image super-resolution (SISR) methods would not
perform well if the assumed degradation model deviates from those in real images. Although …

Test-time training (TTT) through self-supervised learning (SSL) is an emerging paradigm to
tackle distributional shifts. Despite encouraging results, it remains unclear when this …

Most existing CNN-based super-resolution (SR) methods are developed based on an
assumption that the degradation is fixed and known (eg, bicubic downsampling). However …

In image-to-image translation, each patch in the output should reflect the content of the
corresponding patch in the input, independent of domain. We propose a straightforward …

The digital transformation in healthcare, propelled by the integration of deep learning
models and the Internet of Things (IoT), is creating unprecedented opportunities for …