Continual image super-resolution (CISR) aims to efficiently adapt a pre-trained model to a variety of tasks while retaining knowledge from previously learned tasks, minimizing the need for intensive independent training. The primary challenges …
Realistic image super-resolution (SR) focuses on transforming real-world low-resolution (LR) images into high-resolution (HR) ones, handling more complex degradation patterns than synthetic SR tasks. This is critical for applications like …
Magnetic Resonance (MR) imaging is essential in clinical diagnostics due to its ability to capture detailed soft tissue structures. However, acquiring high-resolution MR images is expensive and often leads to reduced signal-to-noise ratios. To …
Blind image super-resolution (blind SR) aims to restore a high-resolution (HR) image from a low-resolution (LR) image with unknown degradation. Many existing methods explicitly estimate degradation information from various LR images. However, in most …