---
# System prepended metadata

title: Super Resolution
tags: [Paper Study]

---

###### tags: `Paper Study`

# Super Resolution

###  [2020] Closed-loop Matters- Dual Regression Networks for Single Image Super-Resolution

**Highlights**

1. **Existing SR Problems**: (1) There exist infinite HR images that can be downsampled to the same LR image. Thus the space of the possible functions that map LR to HR images becomes extremely large. (2) SR methods rely on paired data. LR-HR data may be unavailable in real-world.The underlying degradation is unknown. (The real-world data do not have the same distribution to LR images obtained from degradation method, e.g. Bicubic)

2. Dual regression training: (1) Add constraint to LR images (SR images reconstruct LR images) (2) No need HR data. Directly train from the LR images in real-world.

   ![Screen Shot 2020-11-30 at 8.37.00 AM](https://i.imgur.com/5tdIKdh.png)

3. Dual regression training for paired data:

   ![Screen Shot 2020-11-30 at 8.46.09 AM](https://i.imgur.com/X3rtRNd.png)
   
4. Dual regression training for unpaired data:

   ![Screen Shot 2020-11-30 at 8.46.34 AM](https://i.imgur.com/1Jr9aTT.png)

5. Architecture

   ![Screen Shot 2020-11-30 at 8.48.47 AM](https://i.imgur.com/fDy7G8A.png)

6. TODO results, model size

**Ideas**

1. Dual regression for MFSR/SISR.

   ![Screen Shot 2020-11-30 at 4.46.03 AM](https://i.imgur.com/Nx8kvSi.png)

2. Dual regression + Crop segmentation ROI for SISR.

   ![Screen Shot 2020-11-30 at 4.46.28 AM](https://i.imgur.com/qXOJ84O.png)

   


