Exampled-based Super resolution

Presenter: Yu-Wei Fan

Outline

• Introduction• Training set generation• Super-resolution algorithms– Idea– Markov Network– One-pass algorithm

• Results

Outline

• Introduction• Training set generation• Super-resolution algorithms– Idea– Markov Network– One-pass algorithm

• Results

Introduction

• Why do we need high resolution image?• Usually , we cannot get high resolution image easy.

Introduction

• Aim: High Resolution Image– 1.Reduce the pixel size• the amount of light available also decrease• generates shot noise

– 2.Increase the chip size• increase capacitance• difficult to speed up a charge transfer rate

– 3.Signal processing techniques• Low cost

Introduction• General Super Resolution

– Need multi frames information

• Exampled-based Super resolution– Need only one frame

Outline

• Introduction• Training set generation• Super-resolution algorithms– Idea– Markov Network– One-pass algorithm

• Results

Training set generation

•Store the high-resolution patch corresponding to every possible low-resolution image patch.•Typically, these patches are 5 × 5 or 7 × 7 pixels.

Outline

• Introduction• Training set generation• Super-resolution algorithms– Idea– Markov Network– One-pass algorithm

• Results

Idea

Unfortunately, that approach doesn’t work!

Markov Network

Markov Network

• MAP Estimator:

Markov Network

• Example:

Markov Network

• Belief Propagation

Where is from the previous iteration. The initial are 1.Typically, three or four iterations of the algorithm are sufficient.

One-pass algorithm

• How do we select a good patch pair?• Two constraint:– frequency constraint– spatial constraint

One-pass algorithm

Outline

• Introduction• Training set generation• Super-resolution algorithms– Idea– Markov Network– One-pass algorithm

• Results

Results

Results

Results• α=0

Results

• α=0.5

Results• α=5