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