Correcting Camera Shake by Incremental Sparse Approximation
This addresses the challenge of correcting irregular blur patterns in images for photography and computer vision applications, but it is incremental as it builds on existing blind deconvolution methods.
The paper tackled the problem of blind deblurring for images blurred by camera shake by proposing a method using incremental sparse edge approximation, achieving competitive deblurring performance with state-of-the-art benchmarks while being significantly faster and easier to generalize.
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.