[IPOL discuss] [IPOL announce] new article: Spectral Pre-Adaptation for Restoring Real-World Blurred Images using Standard Deconvolution Methods
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A new article is available in IPOL: https://www.ipol.im/pub/art/2022/385/
Chaoqun Dong, Filip Sroubek, and Javier Portilla,
Spectral Pre-Adaptation for Restoring Real-World Blurred Images using
Standard Deconvolution Methods,
Image Processing On Line, 12 (2022), pp. 218–346.
https://doi.org/10.5201/ipol.2022.385
Abstract
Classical blur models are based on simplifying assumptions, namely
shift-equivariance and circular boundary condition (CBC), that rarely
hold in practice. Shift-equivariance means that a shift of the input
produces the same shift of the output, which implies that blur is
spatially invariant and image aliasing is not present. The CBC assumes
that the image is rectangular and periodically repeating. Discrepancies
between simplified models and real blurred observations cause strong
artifacts in image restoration. The common remedy is to increase the
model complexity and remove simplifying assumptions. However, this also
brings extra computational complexity to the restoration task. We
present spectral pre-adaptation (SPA) that pre-processes blurred images
so they can be restored using fast standard deconvolution algorithms
suitable for simplified models. The SPA serves as a connector between
classical deconvolution methods and a variety of real observations
involving blur. Experiments on simulated and real images show that
standard deconvolution of SPA-interpolated images not only greatly
reduces artifacts compared to direct deconvolution, but performs on a
par with more complex restoration methods.
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