Generation of super-resolution for medical image via a self-prior guided Mamba network with edge-aware constraint

Keyphrases

• Super-resolution 100%
• Medical Image 100%
• Edge-based 100%
• Mamba 100%
• Prior-Guided 100%
• Long-range Dependence 75%
• Convolutional Neural Network 50%
• U-Net 50%
• Neural Network 25%
• Network Approach 25%
• Image Features 25%
• Deep Learning Methods 25%
• Edge Information 25%
• Upsampling 25%
• Encoder 25%
• Computational Burden 25%
• State-space Model 25%
• Global Features 25%
• Vision Task 25%
• Feature Representation 25%
• Generation Approach 25%
• Quadratic Complexity 25%
• Texture Detail 25%
• Super-resolution Image 25%
• Medical Dataset 25%
• Transformer-based 25%
• Multiscale Edge 25%
• Learning-based Super-resolution 25%
• Texture Boundary 25%
• Brain Tumor Segmentation 25%
• Neural Transformer 25%
• Dice Score 25%
• Medical Image Super-resolution 25%
• Multi-scale Local Features 25%

Computer Science

• super resolution 100%
• Range Dependency 50%
• Convolutional Neural Network 33%
• image feature 16%
• Edge Information 16%
• Resolution Image 16%
• State Space 16%
• local feature 16%
• Resolution Performance 16%
• Global Feature 16%
• U-Net 16%
• Tumor Segmentation 16%
• Upsampling 16%
• Deep Learning Method 16%
• Multiscale Edge 16%