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 …
Visible watermark removal is crucial for evaluating watermark robustness and advancing more resilient protection techniques. Current methods face challenges in real-world scenarios due to architectural constraints in multi-task frameworks and …
This paper aims to restore original background images in watermarked videos, overcoming challenges posed by traditional approaches that fail to handle the temporal dynamics and diverse watermark characteristics effectively. Our method introduces a …
Visible watermark removal which involves watermark cleaning and background content restoration is pivotal to evaluate the resilience of watermarks. Existing deep neural network (DNN)-based models still struggle with large-area watermarks and are …
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 …