16421: Vision Sensors

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16421 Vision Sensors

• Lecture 7 High Dynamic Range Imaging
• Instructor S. Narasimhan
• Wean 5312, T-R 130pm 300pm

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• High Dynamic Range Imaging
• and Tone Mapping

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Paul Debevecs SIGGRAPH Course
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The Problem of Dynamic Range

• Dynamic Range Range of brightness values
  measurable with a camera

(Hood 1986)

• Todays Cameras Limited Dynamic Range

High Exposure Image
Low Exposure Image

• We need about 5-10 million values to store all
  brightnesses around us.
• But, typical 8-bit cameras provide only 256
  values!!

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High Dynamic Range Imaging

• Capture a lot of images with different exposure
  settings.
• Apply radiometric calibration to each camera.
• Combine the calibrated images (for example,
  using averaging weighted by exposures).

(Mitsunaga)
(Debevec)
Images taken with a fish-eye lens of the sky show
the wide range of brightnesses.
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Relationship between Scene and Image Brightness

• Before light hits the image plane

Scene Radiance L
Image Irradiance E
Lens
Scene
Linear Mapping!

• After light hits the image plane

Camera Electronics
Image Irradiance E
Measured Pixel Values, I
Non-linear Mapping!
Can we go from measured pixel value, I, to
scene radiance, L?
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Radiometric Calibration

• Important preprocessing step for many vision and
  graphics algorithms such as
• photometric stereo, invariants, de-weathering,
  inverse rendering, image based rendering, etc.

• Use a color chart with precisely known
  reflectances.

255
?
Pixel Values
g
0
?
0
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3.1
9.0
19.8
36.2
59.1
90
Irradiance const Reflectance

• Use more camera exposures to fill up the curve.
• Method assumes constant lighting on all patches
  and works best when source is
• far away (example sunlight).
• Unique inverse exists because g is monotonic and
  smooth for all cameras.

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Greg Ward
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Greg Ward
Greg Ward
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