216 research outputs found
Fast and easy blind deblurring using an inverse filter and PROBE
PROBE (Progressive Removal of Blur Residual) is a recursive framework for
blind deblurring. Using the elementary modified inverse filter at its core,
PROBE's experimental performance meets or exceeds the state of the art, both
visually and quantitatively. Remarkably, PROBE lends itself to analysis that
reveals its convergence properties. PROBE is motivated by recent ideas on
progressive blind deblurring, but breaks away from previous research by its
simplicity, speed, performance and potential for analysis. PROBE is neither a
functional minimization approach, nor an open-loop sequential method (blur
kernel estimation followed by non-blind deblurring). PROBE is a feedback
scheme, deriving its unique strength from the closed-loop architecture rather
than from the accuracy of its algorithmic components
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
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