Model Framework
Pre-upsampling SR
Post-upsampling SR
Progressive Upsampling SR
Iterative Up-and-down upsampling SR
Upsampling Methods
Interpolation-based methods
Nearest Neighbor
Bilinear
Bicubic
Others
Learning-based Methods
Transposed Convolution
Sub-pixel Layer
Meta Upscale Module
Network Design
Residual Learning
Recursive Learning
Multi-path Learning
Dense Connection
Attention Mechanism
Advanced Convolution
Region-recursive Learning
Pyramid Pooling
Wavelet Transformation
xUnit
Desubpixel
Learning Strategies
Loss Functions:
- Pixel Loss
- Content Loss
- Texture Loss
- Adversarial Loss
- Cycle Consistency Loss
- Total Variation Loss
- Prior-based Loss
Batch Normalization
Curriculum Learning
Multi-supervision
Other Improvement
Context-wise Network Fusion
Data Augmentation
Multi-task Learning
Network Interpolation
Self-ensemble
