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