Gradient Domain High Dynamic Range Compression Raanan Fattal Dani Lischinski Michael Werman.
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Transcript of Gradient Domain High Dynamic Range Compression Raanan Fattal Dani Lischinski Michael Werman.
Gradient Domain High Gradient Domain High Dynamic Range CompressionDynamic Range Compression
Raanan FattalRaanan Fattal
Dani LischinskiDani Lischinski
Michael WermanMichael Werman
The Dynamic Range ProblemThe Dynamic Range Problem
What’s wrong with What’s wrong with these images?these images?
What would your eye What would your eye see?see?
How could you put all How could you put all this information into this information into one image?one image?
Whole Image SolutionsWhole Image Solutions
Tone Reproduction CurvesTone Reproduction CurvesRe-mapping of luminance valuesRe-mapping of luminance valuesEasy to computeEasy to computeSuffer from quantizationSuffer from quantization
ExamplesExamplesLinear scalingLinear scalingGamma correctionGamma correctionMore sophisticated models…More sophisticated models…
Ward Larson ModelWard Larson Model
One of the best total One of the best total image methodsimage methods
Based on models of Based on models of display capabilities display capabilities and human visionand human vision
Still suffers from loss Still suffers from loss of local contrastof local contrast
Notice washed-out Notice washed-out appearance of the appearance of the outside areaoutside area
Local SolutionsLocal Solutions
Tone Reproduction OperatorsTone Reproduction OperatorsTake local context into accountTake local context into accountAttempt to solve the local contrast problemAttempt to solve the local contrast problem
Older MethodsOlder MethodsBased on estimating illuminance and Based on estimating illuminance and
reflectance for each part of the imagereflectance for each part of the imageSuffer from artifacts, dark halosSuffer from artifacts, dark halos
Low Curvature Image SimplifierLow Curvature Image Simplifier
Tumblin and Turk, Tumblin and Turk, 19991999
Scale luminance of Scale luminance of smoothed imagesmoothed image
Add back detailsAdd back details 8 parameters8 parameters Computationally Computationally
intensiveintensive
Gradient Domain MethodGradient Domain Method
Basic AssumptionsBasic Assumptions
The eye responds more to local intensity The eye responds more to local intensity differences than global illuminationdifferences than global illumination
A HDR image must have some large A HDR image must have some large magnitude gradientsmagnitude gradients
Fine details consist only of smaller Fine details consist only of smaller magnitude gradients magnitude gradients
Basic MethodBasic Method
Take the log of the luminancesTake the log of the luminancesCalculate the gradient at each pointCalculate the gradient at each pointScale the magnitudes of the gradients with Scale the magnitudes of the gradients with
a progressive scaling function (Large a progressive scaling function (Large magnitudes are scaled down more than magnitudes are scaled down more than small magnitudes)small magnitudes)
Re-integrate the gradients and invert the Re-integrate the gradients and invert the log to get the final imagelog to get the final image
1D Example1D Example
Original Signal F(x)Original Signal F(x) - Dynamic range: 2415:1- Dynamic range: 2415:1
1D Example1D Example
ln F(x)ln F(x)
1D Example1D Example
F’(x)F’(x)
1D Example1D Example
G(x) = F’(x) after applying the attenuating functionG(x) = F’(x) after applying the attenuating function
1D Example1D Example
I(x) = Integrate G(x)I(x) = Integrate G(x)
1D Example1D Example
eeI(x)I(x) - New dynamic range: 7.5:1- New dynamic range: 7.5:1
Changes for 2DChanges for 2D
Use gradients instead of derivativesUse gradients instead of derivativesMay produce a non-integrable vector field May produce a non-integrable vector field
after scalingafter scalingTransform scaled vectors into a Transform scaled vectors into a
conservative field whose gradients are conservative field whose gradients are closest to G(x)closest to G(x)
Attenuation MapAttenuation Map
Attenuation DetailsAttenuation Details
Images contain edges at multiple levels of Images contain edges at multiple levels of detaildetail
How do we handle this?How do we handle this?Compute gradients for many different Compute gradients for many different
resolutions of the imageresolutions of the imageThe set of different resolution images The set of different resolution images
composes a Gaussian pyramidcomposes a Gaussian pyramid
Creating the Final ImageCreating the Final Image
How do we recombine the different How do we recombine the different resolution levels?resolution levels?Start with coarsest imageStart with coarsest imageCalculate scaling factorsCalculate scaling factorsLinearly interpolate those factors for each Linearly interpolate those factors for each
point in the next image, and multiply with the point in the next image, and multiply with the local scaling factorlocal scaling factor
Apply the combined factors to the highest Apply the combined factors to the highest resolution imageresolution image
The Attenuation FunctionThe Attenuation Function
αα = average gradient = average gradient magnitude for each magnitude for each level times 0.1level times 0.1
ββ = adjustable gain = adjustable gain (between 0.8 and 0.9)(between 0.8 and 0.9)
PerformancePerformance
On an 1800 MHz Pentium 4:On an 1800 MHz Pentium 4:Computing a 512x384 image takes 1.1 Computing a 512x384 image takes 1.1
secondssecondsComputing a 1024x768 image takes 4.5 Computing a 1024x768 image takes 4.5
secondssecondsLCIS takes 8.5 minutes to compute a LCIS takes 8.5 minutes to compute a
751x1130 image751x1130 image
ExamplesExamples
Streetlight on a foggy Streetlight on a foggy nightnight
Dynamic range Dynamic range 100,000:1100,000:1
ExamplesExamples
Stanford Memorial Stanford Memorial ChurchChurch
DR 250,000:1DR 250,000:1
ApplicationsApplications
Enhancing contrast for LDR imagesEnhancing contrast for LDR imagesCombining photographs of different Combining photographs of different
exposure levels to enhance detail or stitch exposure levels to enhance detail or stitch together for panoramastogether for panoramas
Medical image enhancementsMedical image enhancements
PanoramasPanoramas
Medical ImagingMedical Imaging
Questions / CreditsQuestions / Credits
Any questions?Any questions?
All pictures in this presentation are from All pictures in this presentation are from the original paperthe original paper