Hdr image construction from multi exposed stereo ldr images
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HDR Image Construction from Multi-exposed Stereo LDR Images. Ning Sun, Hassan Mansour, Rabab Ward Proceedings of 2010 IEEE 17th International Conference on Image Processing September 26-29, 2010, Hong Kong. Andy { [email protected] }. Algorithm description.

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HDR Image Construction from Multi-exposed Stereo LDR Images

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Hdr image construction from multi exposed stereo ldr images

HDR Image Construction from Multi-exposed Stereo LDR Images

Ning Sun, Hassan Mansour, Rabab Ward

Proceedings of 2010 IEEE 17th International Conference on Image Processing

September 26-29, 2010, Hong Kong

Andy [email protected]}


Algorithm description

Algorithm description

Two LDR images with different exposures

Camera response function

Radiance maps of LDR images

Refined disparity map

HDR image

Initial disparity map

Main concept:

1. Multi-exposed stereo images are captured using identicalcameras placed adjacent to each other on a horizontal line.

2. Stereo matching is then used to find a disparity map thatmatches each pixel in one image to the corresponding pixelin another image.

3. A subset of the matched pixels is used to generate the cameraresponse function which in turn is used to generate the sceneradiance map for each view with an expanded dynamic range.

4. The disparity map is refined by performing a second stereomatching stage using the radiance maps


Imaging models

Imaging models

Left image

Right image

Correction factor

Left image

Right image

Scene radiance

Exposure ration between images

Exposure ration between images

Scene radiance

Imaging models are used to determine the scene radiance from the measured pixel data

Gamma-correction model

Polynomial camera response


Computing the disparity map

Computing the disparity map

Best disparity map

Set of feasible disparities

Dissimilarity term

Smoothing term

Used for initial disparity estimation

Pixel dissimilarity

Disparity smoothness


Pixel dissimilarity

Pixel dissimilarity

Spatial smoothing

Intensity smoothing

I’ - intensity in log space defined as:

- Search window centered on p

- Bilateral weight

- displacement


Pixel dissimilarity1

Pixel dissimilarity


Disparity smoothness

Disparity smoothness

Initial disparity and camera response

1. Minimize using graph cut algorithm

2. Compute polynomial coefficients for camera response function


Error correction

Error correction

Convert images to radiance space (results should be same for both images)

Minimize energy function one more time with different dissimilarity function

For valid pixels

For erroneous pixels

Hamming distance between pixels p and p+fp after applying Census transform


Input ldr images

Input LDR images


Disparity maps

Disparity maps

Reference disparity map

Initial disparity estimation

Final map


Hdr images

HDR images


Experimental results

Experimental results


Conclusions

Conclusions

Disparity map computation algorithm is proposed

Proposed method is able to compute disparity between differently exposed images

Can deal with saturated regions in the image

Can be used for capturing motion scenes with different exposures

Disadvantages

  • - High computational costs

  • Generated images are slightly blurred

  • No rotation is considered


Ideal image formation system

Ideal image formation system

Camera exposure

Radiometric response

Camera response function

Shutter speed

or

Image brightness

Sensor response

Where

Reverse camera response function

L

Irradiance

I

Response = Gray-level

From optics

Aperture

Angle from ray to optical axis

Image radiance

Focal length

Scene radiance


Response function examples

Response function examples

Response functions of a few popular cameras provided by their manufacturers


Graph cut algorithm

Graph-cut algorithm

1. Start with an arbitrary labeling f

2. Set success := 0

3. For each label 2 L

3.1. Find f*= argminE(f’) among f’withinoneα-expansion of f

3.2. If E(f*) < E(f), set f := f* and success := 1

4. If success = 1 goto 2

5. Return f


Census transform

Census transform

If (CurrentPixelIntensity<CentrePixelIntensity) boolean bit=0

else boolean bit=1

Input image

3x3 transform

5x5 transform


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