|
|
|
| e-Pub |
Previous | 
Home
Section: New Results
Image restoration, manipulation and enhancement
Fast Local Laplacian Filters: Theory and Applications
Participants : Mathieu Aubry, Sylvain Paris [Adobe] , Samuel Hasinoff [Google] , Jan Kautz [University College London] , Fredo Durand [MIT] .
Multi-scale manipulations are central to image editing but they are also prone to halos. Achieving artifact-free results requires sophisticated edge-aware techniques and careful parameter tuning. These shortcomings were recently addressed by the local Laplacian filters, which can achieve a broad range of effects using standard Laplacian pyramids. However, these filters are slow to evaluate and their relationship to other approaches is unclear. In this work, we show that they are closely related to anisotropic diffusion and to bilateral filtering. Our study also leads to a variant of the bilateral filter that produces cleaner edges while retaining its speed. Building upon this result, we describe an acceleration scheme for local Laplacian filters on gray-scale images that yields speed-ups on the order of 50x. Finally, we demonstrate how to use local Laplacian filters to alter the distribution of gradients in an image. We illustrate this property with a robust algorithm for photographic style transfer. This work has been published at ACM Transactions on Graphics 2014 [2] .
Learning a Convolutional Neural Network for Non-uniform Motion Blur Removal
Participants : Jian Sun, Wenfei Cao, Zongben Xu, Jean Ponce.
In work work, we address the problem of estimating and removing non-uniform motion blur from a single blurry image. We propose a deep learning approach to predicting the probabilistic distribution of motion blur at the patch level using a convolutional neural network (CNN). We further extend the candidate set of motion kernels predicted by the CNN using carefully designed image rotations. A Markov random field model is then used to infer a dense non-uniform motion blur field enforcing the motion smoothness. Finally the motion blur is removed by a non-uniform deblurring model using patch-level image prior. Experimental evaluations show that our approach can effectively estimate and remove complex non-uniform motion blur that cannot be well achieved by the previous approaches. This work has been submitted to CVPR 2015.