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 Internal camera distortions

Point Spread Function (PSF) Assume: Point light source PSF =

Convolution model motivation B = K  L + N Convolution model motivation Assume: No image plane rotation No object motion during the exposure No significant parallax (depth variation) Camera motion is Pure Translation!!! Violation of assumption:

Convolution model motivation B = K  L + N Convolution model motivation Assume: No image plane rotation No object motion during the exposure No significant parallax (depth variation) Camera motion is Pure Translation!!! Experimental validation: 8 subjects handholding DSLR with 1 sec exposure Close-up of dots

Convolution Model  + Notations Generation rule: B = K  L + N L: original image K: the blur kernel (PSF) N: sensor noise (white) B: input blurred image  + Generation rule: B = K  L + N

How can the image be recovered? Assumptions: Known kernel (PSF) Constant kernel for the whole image No noise Goal: Recover L s.t.: B = K  L Fourier Convolution Theorem!

De-blur using Convolution Theorem

Example: PSF Blurred Image Recovered

Noisy case: Example: Deconvolution is unstable

Regularization is required 1D example: FT of original signal Original signal Reconstructed FT of the signal Convolved signals w/w noise FT of convolved signals Regularization is required

Regularizing by window Window size: 51 151 191