Fast Approximate Defocus Deconvolution

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Abstract:

We present a fast, simple, and parallelizable deconvolution algorithm for the real-time deblurring of one- or two- dimensional signals (i.e. images) degraded by defocus or bokeh-like blur. The proposed algorithm runs in linear- time and performs significantly faster than other popular deconvolution methods tested, bringing the deblurring time down to under 10ms for full-HD images. It has a simple software implementation, requiring no Fourier transforms or dynamic memory allocation. Its parallel design makes it especially suitable for GPU acceleration. For one-dimensional noise-free signals, the algorithm is proven to converge exactly to the original un-blurred signal.

Presentation:

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Citation:

Daniel Williams, "A fast, simple, and parallelizable deconvolution algorithm for real-time applications," Proc. SPIE 12674, Applications of Digital Image Processing XLVI, 126740B (4 October 2023);
http://dx.doi.org/10.1117/12.2677259

Code on GitHub:

Open Source Fast-Deblur on GitHub

Example Videos:

This algorithm can extract details and information that would otherwise be lost in the defocus blur.
And it can do it in real-time!