[IPOL announce] new article: Spectral Pre-Adaptation for Restoring Real-World Blurred Images using Standard Deconvolution Methods

[announcements about the IPOL journal]

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.