Correcting Camera Shake by Incremental Sparse Approximation

TL;DR

This paper introduces a new blind deconvolution technique that incrementally refines the blur kernel estimate by focusing on edges of varying strength, improving speed and generalization in camera shake correction.

Contribution

The proposed method uses incremental sparse edge approximation to enhance deblurring performance and efficiency compared to existing state-of-the-art approaches.

Findings

Achieves competitive deblurring quality with faster processing times.

Effectively refines blur kernel estimates by progressively incorporating weaker edges.

Outperforms previous methods in speed and ease of generalization.

Abstract

The problem of deblurring an image when the blur kernel is unknown remains challenging after decades of work. Recently there has been rapid progress on correcting irregular blur patterns caused by camera shake, but there is still much room for improvement. We propose a new blind deconvolution method using incremental sparse edge approximation to recover images blurred by camera shake. We estimate the blur kernel first from only the strongest edges in the image, then gradually refine this estimate by allowing for weaker and weaker edges. Our method competes with the benchmark deblurring performance of the state-of-the-art while being significantly faster and easier to generalize.