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Ghost-FreeHigh Dynamic Range Imaging

University of Trento

Zerihun, Bisrat Alene

Advisor:Nicola Conci, PhD, Ass. Prof, University of TrentoNicola Piotto, PhD, HUAWEI-ERCGiovanni Cordara, PhD, HUAWEI-ERC

INTRODUCTION1.Problem statement2.

3. Proposed approach

4. Result

5. Conclusion

IntroductionWhat is dynamic range in photography?

the ratio between the brightest and the darkest areas of a given scene.

●Human visual system (HVS) can observe 3 to 5 (10,000: 1) orders of luminance variations at a time

●cameras have a much lower dynamic range 2 to 3 (1000:1) order of luminance variation8-bit camera can capture 256:1 luminance range

Dynamic Range

Outdoor-Indoor Scene

Human Visual System HVS

100,000:1

10,000:1 1,000:1Camera

Why do we need High Dynamic Range Imaging ?

Under-exposed

Under-exposed image Over-exposed image

• To eliminate saturated areas• To encompass large number of luminance variation (HDR)

How does HDR imaging works ?

●Step 1: Image acquisition■Capture two or more low dynamic range (LDR) images of a scene

● Step 2: Fuse the LDR images Exposure fusion [1]

It preserves the best pixels of a given scene

Multiply & add multi-

scale resolutions

Generate Multi-scale resolution(Pyramid)

Merge the pyramid

Compute weight

Input image

sequence

[1] T. Mertens, J. Kautz, and F. V. Reeth. Exposure fusion. Pacific Graphics, 2007.

• Compute weight value for each pixel (ij) in each image

Control factors

Weight

Contrast

Saturation

Well-exposedness

Input image

sequence

Multiply & add multi-

scale resolutions

Generate Multi-scale resolution(Pyramid)

Merge the pyramid

Compute weight

Input Images

Fused Image

Input image

sequence Compute weight

Generate Multi-scale resolutions(Pyramid)

Multiply & add

multi-scale resolutions

Merge the pyramid

Image Laplacian Pyramid

Weight map Gaussian Pyramid

Fused pyramid

Gaussian Pyramid

Laplacian Pyramid

Fused Pyramid

2.

Introduction1.

3. Proposed approach

4. Result

5. Conclusion

PROBLEM STATEMENT

Ghost problem

Computational Complexity

3.

Introduction1.

2. Problem Statement

4. Result

5. Conclusion

PROPOSED APPROACH

Proposed Approach

• Ghost-Free Fast Exposure Fusion Computational complexity Ghost Problem

Weight Adjustmen

tExposure

FusionMotion

Detection

Down sample input

images IL

Binary motion map M

Motion Detection

Weight Adjustmen

tExposure

Fusion

Down sample input

images IL

Motion Detection

Weight adjustment

Select a reference image

Down sample input

images IL

Motion Detection

Weight Adjustmen

tExposure

Fusion

Weight Adjustmen

tMotion

Detection

Down sample input

images IL

• Low resolution image Rlow

Exposure Fusion

• Up sample Rlow• Blurred• Artifacts around the moving objects

•Compute the missing detail

Motion map

Up sampled Low

resolution fused image

Amplification factors

Missing detail

HDR-like image

Original image

Up sampled image

•Generate final HDR-like image

•Before applying weight adjustment

•After applying weight adjustment

4.

Introduction1.

2. Problem Statement

3. Proposed Approach

5. Conclusion

RESULT

Result

Image Resolution

Technique

(695x555)

(1022x679) (1024x754) (900x1350)

(2048x1216)

(2048x2016)

De-ghosting + FEF 0.208 sec 0.257 sec 0.366 sec 0.487 sec 0.806 sec 1.003 sec

De-ghosting + EF 0.45 sec 0.894 sec 0.925 sec 1.467 sec 2.2 sec 2.788 sec

•Testing environment: • Computer - Lenovo ThinkCenter • Processor – Intel(R) core (TM) i5-3470 CPU @3.20GH• RAM- 4GB • Operating System – Windows 7, 32 bit

• 3 times faster

Pure EFBMDProposed technique

5.

Introduction1.

2. Problem Statement

3. Proposed Approach

4. Result

CONCLUSION

Conclusion Faster Ghost-free HDR imaging

Computationally less expensive Ghost-free

Applicable for • mobile phones • video conferencing• HDR Panorama …

Future Works• Improve motion detection algorithms• Reduce multi-scale resolution (Pyramid) level

Thank you!!

Pure EFPhotomatixProposed Technique