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Removing motion blur from a single image. Sources of blur. Object motion. Sources of blur. Object motion Translation of camera. Sources of blur. Object motion Translation of camera Rotation of camera. Sources of blur. Object motion Translation of camera Rotation of camera. Defocus - PowerPoint PPT Presentation
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Removing motion blur Removing motion blur from a single imagefrom a single image
Sources of blurSources of blur
• Object motion
Sources of blurSources of blur
• Object motion
• Translation of camera
Sources of blurSources of blur
• Object motion
• Translation of camera
• Rotation of camera
• Defocus
• Internal camera distortions
Sources of blurSources of blur
• Object motion
• Translation of camera
• Rotation of camera
Point Spread Function (PSF)Point Spread Function (PSF)
Assume:• Point light source
PSFPSF ==
Convolution model motivationConvolution model motivation• Assume:
– No image plane rotation– No object motion during the exposure– No significant parallax (depth variation)
• Violation of assumption:
Camera motion isCamera motion is
Pure Translation!!!Pure Translation!!!
Convolution model motivationConvolution model motivation• Assume:
– No image plane rotation– No object motion during the exposure– No significant parallax (depth variation)
Camera motion isCamera motion is
Pure Translation!!!Pure Translation!!!
Close-up of dots
8 subjects handholding DSLR with 1 sec exposure
• Experimental validation:
Convolution ModelConvolution Model• Notations
– L: original image
– K: the blur kernel (PSF)
– N: sensor noise (white)
– BB: input blurred image
Generation rule: B B = K L + N
+
How can the image be How can the image be recovered?recovered?
Assumptions:• Known kernel (PSF)• Constant kernel for the
whole image• No noise
Goal:• Recover L s.t.:
Fourier Fourier Convolution Convolution Theorem!Theorem!
B B = K L
De-blur using Convolution De-blur using Convolution Theorem Theorem
B KL KB L
/BL K
Convolution Theorem: f g f g
1 /BL K
LB K
Example:Example:
PSFPSF BlurredBlurredImageImage
RecoveredRecovered
Noisy case:Noisy case:
1 /BL K
Example:Example: 0, 0.001
DeconvolutionDeconvolutionis unstableis unstable
1 / /K KL B N
1D 1D example:example:
Regularization is requiredRegularization is required
Original signalOriginal signalFT of original signalFT of original signal
Convolved signals w/w noiseConvolved signals w/w noiseFT of convolved signalsFT of convolved signals
Reconstructed FT of the Reconstructed FT of the signalsignal
RegularizinRegularizing g by window by window
Window size:
51 151 191