Abstract
This work addresses the problem of blind image deblurring, that is, of recovering an original image observed through one or more unknown linear channels and corrupted by additive noise. We resort to an iterative algorithm, belonging to the class of Bussgang algorithms, based on alternating a linear and a nonlinear image estimation stage. In detail, we investigate the design of a novel nonlinear processing acting on the Radon transform of the image edges. This choice is motivated by the fact that the Radon transform of the image edges well describes the structural image features and the effect of blur, thus simplifying the nonlinearity design. The effect of the nonlinear processing is to thin the blurred image edges and to drive the overall blind restoration algorithm to a sharp, focused image. The performance of the algorithm is assessed by experimental results pertaining to restoration of blurred natural images.
Highlights
Image deblurring has been widely studied in literature because of its theoretical as well as practical importance in fields such as astronomical imaging [1], remote sensing [2], medical imaging [3], to cite only a few
We investigate the design of a novel nonlinear processing acting on the Radon transform of the image edges
We investigate the design of the nonlinear processing stage using the Radon Transform (RT) [15] of the image edges
Summary
Image deblurring has been widely studied in literature because of its theoretical as well as practical importance in fields such as astronomical imaging [1], remote sensing [2], medical imaging [3], to cite only a few. For example, many differently focused versions of the same image are acquired during a single experiment, due to an intrinsic tradeoff between the bandwidth of the imaging system and the contrast of the resulting image In other applications, such as telesurveillance, multiple observed images can be acquired in order to better counteract, Blind Image Nonlinear Deblurring in the Edge Domain x[m, n]. In [7], it is shown that, under some mild assumptions, both the filters and the image can be exactly determined from noise-free observations as well as stably estimated from noisy observations Both in [7, 8], the channel estimation phase precedes the restoration phase. We investigate the design of the nonlinear processing stage using the Radon Transform (RT) [15] of the image edges.
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