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Dynamic Range Dynamic Range Compression Compression & Color Constancy & Color Constancy Democritus University of Thrace Democritus University of Thrace 2006 2006

Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

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Page 1: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Dynamic Range Dynamic Range CompressionCompression& Color Constancy& Color Constancy

Dynamic Range Dynamic Range CompressionCompression& Color Constancy& Color Constancy

Democritus University of Thrace Democritus University of Thrace 2006 2006

Democritus University of Thrace Democritus University of Thrace 2006 2006

Page 2: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Dynamic Range: The ratio between the maximum and the minimum tonal values in an image (cd/m2)

Commercial cameras have only a dynamic range of 256:1 (the maximum value is 256 times greater than the minimum value that they can capture)

Scenes with grater dynamic range than 256:1 are not captured correctly (intensities are clipped)

Dynamic RangeDynamic Range

Page 3: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

The dynamic range of natural scenes is a lot more than 256:1(object in frond of a backlight)

The camera can capture correctly only the bright or the dark area, but never both of them (underexposure, overexposure)

The Dynamic Range ProblemThe Dynamic Range Problem

Underexposured

(no visible details)

Normaly

exposuredOverexposured

(no visible details)

Normaly

exposured

Page 4: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Images with dynamic range problemImages with dynamic range problem

Page 5: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Our approach –center surroundOur approach –center surround

surround

center

CenterSurround

Every pixel (center) is compared with its neighborhood (surround) and is assigned a new value, in order to maximize the contrast in the dark regions of the image

Surround is the average intensity value of the neighborhood (0-255)

Center is the intensity of the pixel (0-255)

Page 6: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Center-surround transfer functionCenter-surround transfer function

CenterSurround

In a dark image region (surround is small)

When the center is dark

23

85Before:

Surround 18

Center 23

After:

Surround 18

Center 85

18

Page 7: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Center-surround transfer functionCenter-surround transfer function

CenterSurround

In a dark image region (surround is small)

When the center is bright

241

245

Before:

Surround 18

Center 241

After:

Surround 18

Center 245

18

Page 8: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Center-surround transfer functionCenter-surround transfer function

CenterSurround

In a bright image region (surround is high)

When the center is dark

36

36

Before:

Surround 240

Center 36

After:

Surround 240

Center 36240

Page 9: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Center-surround transfer functionCenter-surround transfer function

CenterSurround

In a bright image region (surround is high)

When the center is bright

244

244

Before:

Surround 240

Center 244

After:

Surround 240

Center 244240

Page 10: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ConclusionConclusion

CenterSurround

In a dark image region (shadows or underexposured areas) the value of the pixel is increased relatively its neighborhood, increasing the local contrast

In a bright image region (normally exposured areas) the value of the pixel is unchangeable

Page 11: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults

Page 12: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults

Page 13: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults

Page 14: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults

Page 15: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults

Page 16: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

The unknown illuminant problemThe unknown illuminant problem

whitewhite

whitewhite

whitewhite

The HVS has a degree of color constancy

Page 17: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Images under color illuminantImages under color illuminant

Incandescent lights

Green water Fluorescent light

Page 18: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

The specularity problemThe specularity problemSpecularities and direct light sources have a greater intensity than the response to pure white

Which intensity is the response to pure white?

Page 19: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

Our approach: estimate the white

response

Our approach: estimate the white

response

Page 20: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults

Page 21: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults

Page 22: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults

Page 23: Dynamic Range Compression & Color Constancy Democritus University of Thrace 2006 2006

ResultsResults