Abstract

Blind deconvolution of motion blur is hard, but it can be made easier if multiple images are available. This observation, and an algorithm using two differently-blurred images of a scene are the sub ject of this paper. While this idea is not new, existing methods have so far not delivered very practical results. In practice, the PSFs corresponding to the two given images are estimated and assumed to be close to the latent motion blurs. But in actual fact, these estimated blurs are often far from the truth, for a simple reason: They often share a common, and unidentified PSF that goes unaccounted for. That is, the estimated PSFs are themselves “blurry”. While this can be due to any number of other blur sources including shallow depth of field, out of focus, lens aberrations, diffraction effects, and the like, it is also a mathematical artifact of the ill-posedness of the deconvolution problem. In this paper, instead of estimating the PSFs directly and only once from the observed images, we first generate a rough estimate of the PSFs using a robust multichannel deconvolution algorithm, and then “deconvolve the PSFs” to refine the outputs. Simulated and real data experiments show that this strategy works quite well for motion blurred images, producing state of the art results.