Spectral Pre-Adaptation for Restoring Real-World Blurred Images Using Standard Deconvolution Methods
Chaoqun Dong, Filip Sroubek, Javier Portilla
⚠ This is a preprint. It may change before it is accepted for publication.


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.