Grisha Vaksman (Technion) - CANCELLED!
חדר 337, בניין טאוב למדעי המחשב
In recent years much work has been devoted to the development of image processing
algorithms using local patches. The main idea in this line of work is to impose a
statistical prior on the patches of the desired image. An algorithm following this path
extracts all possible patches with overlaps from the image, and operates on each
separately. The more advanced algorithms exploit also interrelations between different
patches in the reconstruction process.
In this work we further study the interrelations between patches, and harness it to
propose a simple yet effective regularization for image restoration problems. Our
approach builds on the classic Maximum a posteriori probability (MAP) estimator,
while using a novel permutation-based regularization term, following the work of Ram
et. al. (2014). The permutation is obtained by a crude patch-ordering operation, and the
prior employed within the MAP forces smoothness along the 1D pixel-path obtained.
We demonstrate the success of the proposed scheme on a diverse set of problems: (i)
severe Poisson image denoising, (ii) Gaussian image denoising, (iii) image deblurring,
and (iv) single image super-resolution