Low-level Image Processing

1. Dual-domain Adaptation Networks for Realistic Image Super-resolution

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 …

2. High-frequency structure transformer for magnetic resonance image super-resolution

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 …

3. PatchWiper: Leveraging Dynamic Patch-Wise Parameters for Real-World Visible Watermark Removal

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 …

4. Decouple and Couple: Exploiting Prior Knowledge for Visible Video Watermark Removal

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 …

5. Bridging Knowledge Gap between Image Inpainting and Large-Area Visible Watermark Removal

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 …

6. Unsupervised Degradation Representation Aware Transform for Real-World Blind Image Super-Resolution

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 …

7. Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal

8. Cross-Modality High-Frequency Transformer for MR Image Super-Resolution

9. Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis

10. PNEN: Pyramid Non-Local Enhanced Networks

Existing neural networks proposed for low-level image processing tasks are usually implemented by stacking convolution layers with limited kernel size. Every convolution layer merely involves in context information from a small local neighborhood. …