Blind Deconvolution from Differently Exposed Image Pairs

(From left to right) Observed blurred image simulating a long-exposure acquisition; observed noisy image simulating a short-exposure acquisition; restored image using the proposed algorithm; (top) original blur PSF, (bottom) blur point spread function estimated by the proposed method.

Photographs acquired under low-light conditions require long exposure times and therefore exhibit significant blurring due to the shaking of the camera. Using shorter exposure times results in sharper images but with a very high level of noise. In this work we address this problem and present a novel blind deconvolution algorithm for a pair of differently exposed images. We formulate the problem in a hierarchical Bayesian framework by utilizing prior knowledge on the unknown image and blur, and also on the dependency between two observed images. By incorporating a fully Bayesian analysis, the developed algorithm estimates all necessary algorithm parameters along with the unknowns, such that no user-intervention is needed. Moreover, we employ a variational Bayesian inference procedure, which allows for the statistical compensation of errors occurring at different stages of the restoration, and also provides uncertainties of the estimates. The proposed algorithm provides high quality restored images and estimated blurs with both synthetic and real images.

Preliminary results of this work have been submitted to IEEE International Conference on Image Processing 2009 (ICIP'09), and a journal paper is currently under review (IEEE Transactions on Image Processing).

Example restoration results from real images taken by a Canon 40D DSLR camera. (Top left) Image acquired with a long exposure time (1 s); (Top right) Image acquired with a short exposure time (0.01 s); (Bottom left) Restored image with our algorithm; (Bottom right) Estimated blur point spread function.