Eliminate Blurry Images Caused by Camera Shake and Movement

Application

Motion blur due to *camera shake is one of the predominant sources of image degradation in handheld photography. Blind image de-blurring remains a challenging problem for modern artificial neural networks. Unlike other image restoration problems, neural network architectures for de-blurring fall behind the performance of existing de-blurring algorithms in case of uniform and 3D blur models. This gap follows from the diverse and profound effect that the unknown blur-kernel has on the de-blurring operator.

Our Innovation

We propose a new architecture which breaks the de-blurring network into two parts:  an analysis network (kernel) which estimates the blur; and a synthesis network that uses this kernel to de-blur the image. Unlike existing de-blurring networks, this design allows us to incorporate the blur-kernel expressly into in the network’s training.

Specifically the advantages this technology are: 

New cross-correlation layers that allow for better blur estimations, including unique components that allow the estimate blur to control the action of the synthesis de-blurring action.

When we evaluate this approach over established benchmark datasets, we see the ability to achieve state-of-the-art de-blurring accuracy on various tests, as well a major speed up in runtime.

Results on datasets: https://www.cse.huji.ac.il/~raananf/projects/deblurnets/

Opportunity

Implementation within image editing & communication platforms