Super-Resolution from a Single Image

by Daniel Glasner, Shai Bagon and Michal Irani

Presented by : Lakkhana MallikarachchiSupervised by : Prof. N.D. Kodikara

Authors

Daniel Glasner

› B.Sc and M.Sc at Tel Aviv University, Israel.› Reading for a PhD at The Weizmann Institute of

Science, Israel.› Web - http://www.wisdom.weizmann.ac.il/~glasner/

Shai Bagon

› PhD at The Weizmann Institute of Science, Israel.› Web - http://www.wisdom.weizmann.ac.il/~bagon/

Authors

Michal Irani› Professor in Computer Science at The

Weizmann Institute of Science, Israel.› Research Interests - Computer Vision and Video

Information Analysis› Web - http://www.wisdom.weizmann.ac.il/~irani/

The need of Super Resolution Main approaches of SR Proposed method Experimental results of the proposed

method

Outline

Why Super Resolution?

Low Resolution

High Resolution

Resize

Generating high resolution imagewith more resolving powerusing one or more low resolution

images.

› more resolving power – more details

What Is Super Resolution?

Multi image super resolution

Example based super resolution

Super Resolution Methods

Several images of the same scenery.

Each image will have different information of the same scenery.

Multi Image Super Resolution

Image database with HR/LR image pairs

Replace similar LR patches with corresponding HR patches.

Example Based Super Resolution

+

LR HR

Combine multi image SR with example based SR

Without use external source

Proposed Approach

5x5 pixel image patches

More than 60% of image patches have 9 or more recurrences within same scale or in different scales

Patch Redundancy

Use patch redundancy in same scale to model multi image super resolution problem

Use patch redundancy in different scales to model example based super resolution problem

Proposed Method

Problem Model

L1 L2 L3

H

Problem Model

p

q

Find similar patches within scale Nearest Neighbor

Multi Image to Single Image

How to find cross-scale patch redundancy?

Finding Similar Patches

I0 = L

I-1

I1 = H

Finding Similar Patches

I0 = L

I-1

I1

I2 = H

Finding Similar Patches

FindNN

FindNN

𝑝

~𝑝

I0 = L

I-1

I-2

I1

I2 = H

Finding Similar Patches

FindNN P

are

nt

FindNN Parent

𝑝

~𝑝

I0 = L

I-1

I-2

I1

I2 = H

𝑠𝑙 .~𝑝

Finding Similar Patches

FindNN P

are

nt

Copy

FindNN Parent Copy

𝑝

~𝑝

𝑠𝑙 .~𝑝

𝑠𝑙 .𝑝

I0 = L

I-1

I-2

I1

I2 = H

RGB YIQ Extract Y component (Luminance) Apply SR to Y component Use interpolation methods to I and Q

components (Chrominance) Combine YIQ

Color Images

Experimental Results

Bi-cubic interpolation Proposed Method

LR

Experimental Results

Nearest NeighborProposed Method

LR

Experimental Results

Example BasedProposed Method

LR

Experimental Results

Bi-cubic interpolation Proposed Method

LR

Two main approaches of Super Resolution

Observation about patch redundancy Unified approach of Super Resolution Experimental results

Summery

D. Glasner, S. Bagon and M. Irani, "Super-resolution from a single image," in IEEE 12th International Conference on Computer Vision (ICCV 2009), Kyoto, Japan, Sep. 29 - Oct. 2, 2009, pp. 349-356.

http://www.wisdom.weizmann.ac.il/~vision/SingleImageSR.html

http://cs.brown.edu/courses/csci1950-g/results/final/pachecoj

References

Any Question?

Thank you…