Removing Partial Blur in a Single Image Shengyang Dai and Ying Wu EECS Department, Northwestern...
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Transcript of Removing Partial Blur in a Single Image Shengyang Dai and Ying Wu EECS Department, Northwestern...
Removing Partial Blur in a Single Image
Shengyang Dai and Ying Wu
EECS Department, Northwestern University, Evanston, IL 60208, USA
2009CVPR
OutlineIntroductionGeneration model of partial blur◦ The two-layer model for a clear image◦Motion blur◦Out-of-focus blur◦Unified formulation of partial blurs
Image recovery from partial degradation◦ The objective function◦ Initialization◦ Recovering (F, B, α)
ExperimentsConclusion
IntroductionTwo key issues◦Partial blur estimation ◦Partial deblurring
Generation model of partial blur(1/3)
The two-layer model for a clear image◦I = F α + B(1 − α)
Degraded image is the average over time
F α : clear foreground componentB(1 − α) : clear background componentα : clear soft occlusion mask ,α(x) [0, ∈1] for each pixel x
dI
Generation model of partial blur(2/3)
Motion blur◦Case 1:
foreground object is moving
static background, we have = 0, q = δ.
◦Case 2: background is moving static foreground, we
have = 0, p = δ.
)(txB
)(txF
Out-of-focus blur◦Case 1:
background layer is in focus
foreground layer is out-of-focus
◦Case 2: foreground layer is in
focus background layer is out-
of-focus
Generation model of partial blur(3/3)
Unified formulation of partial blurs
Either the foreground or background layer is not degraded◦p or q is the δ function
Image recovery from partial degradation(1/2)The objective function
Image recovery from partial degradation(1/2)Initialization◦extract the degraded occlusion mask by
using a matting technique ◦the degradation kernels p and q are estimated
by analyzing both and
◦iterate between F , B and α to obtain the final recovery
p
dI p
Experiments
ConclusionRemoving partial blur from a single
image inputA two-layer image model ◦foreground and background layers
Enables high quality recovery and synthesis for real images