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Zero-Shot Super-Resolution using Deep Internal Learning
#4
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flrngel
opened
6 years ago
flrngel
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6 years ago
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
https://arxiv.org/abs/1712.06087
Abstract
1. Introduction
Feature of ZSSR(Zero-Shot Super Resolution)
2. The Power of Internal Image Statistics
3. Image-Specific CNN
3.1. Architecture & Optimization
3.2. Adapting to the Test Image
4.2. The 'Non-ideal' Case
Checkpoints