Image restoration using non-circulant shift-invariant system models

Image restoration is a well studied problem and there are several proposed methods for deblurring and denoising. Recently, there is increasing interest in iterative schemes that employ non-quadratic regularizers, especially edge-preserving like Total Variation (TV) and sparsity promoting like l₁ regularization. Most methods make simplifying assumptions concerning the system model and the most common one is the use of a circulant blurring model because it facilitates using the FFT. In this work we focus on a more realistic non-circulant blurring model and apply existing algorithms for image restoration with non-quadratic regularization, tailored to work with our non-circulant model.