Abstract
One of the most common defects in digital photography is motion blur caused by camera shake. Shift-invariant motion blur can be modeled as a convolution of the true latent image and a point spread function (PSF) with additive noise. The goal of image deconvolution is to reconstruct a latent image from a degraded image. However, ringing is inevitable artifacts arising in the deconvolution stage. To suppress undesirable artifacts, regularization based methods have been proposed using natural image priors to overcome the ill-posedness of deconvolution problem. When the estimated PSF is erroneous to some extent or the PSF size is large, conventional regularization to reduce ringing would lead to loss of image details. This paper focuses on the nonblind deconvolution by adaptive regularization which preserves image details, while suppressing ringing artifacts. The way is to control the regularization weight adaptively according to the image local characteristics. We adopt elaborated reference maps that indicate the edge strength so that textured and smooth regions can be distinguished. Then we impose an appropriate constraint on the optimization process. The experiments’ results on both synthesized and real images show that our method can restore latent image with much fewer ringing and favors the sharp edges.
Highlights
Image blurring is one of the prime causes of poor image quality in digital photography
One of the most common defects in digital photography is motion blur caused by camera shake
This paper focuses on the nonblind deconvolution by adaptive regularization which preserves image details, while suppressing ringing artifacts
Summary
Image blurring is one of the prime causes of poor image quality in digital photography. Existing single image deblurring methods can be further categorized into two classes If both the PSF and the latent image are unknown, the challenging problem is called blind deconvolution. Great progresses have been achieved in the recent years [1,2,3,4], blind case is severely ill-posed problem because the number of unknowns exceeds the number of observed data In contrast to the former, if the PSF is assumed to be known or computed in other ways, the problem is reduced to estimating the latent image alone. We focus on non-blind deconvolution with adaptive regularization that controls the regularization strength according to the image local characteristics This strategy reduces ringing artifacts in a smooth region effectively and preserves image details in a textured region simultaneously. The experimental results show that our nonblind deconvolution can produce latent image with much fewer ringing and preserve the sharp edges
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