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Zero-Shot Super-Resolution using Deep Internal Learning
https://arxiv.org/abs/1712.06087
Abstract
- many paper has restriction on their data
1. Introduction
- this paper talks about "Real LR" means low-rate image on wild
Feature of ZSSR(Zero-Shot Super Resolution)
- train small CNN at test time
- uses CNN to infer HR-LR relation
2. The Power of Internal Image Statistics
- evidence from same image
- predictive
- gets low entropy of internal information
3. Image-Specific CNN
- Pair "I_down_scale" and "I"
3.1. Architecture & Optimization
- Model uses 8 Layer and 64 channels
- uses ReLU
- with 1 increment of Scale(S), 54 seconds takes more to test
3.2. Adapting to the Test Image
- other model's hyperparameter can not be change after train
4.2. The 'Non-ideal' Case
- they made their own dataset
Checkpoints
- Why model uses residual between the interpolated LR and its HR parent?
- What does non-synthesized goes with reliability in this paper?
- This paper can be read with "Deep Prior"
- What effect "Gaussian noise" does?
- Check "Nonparametric blind super-resolution" paper