Blind Space-Variant Single-Image Restoration of Defocus Blur

Bar, Leah; Sochen, Nir; Kiryati, Nahum

doi:10.1007/978-3-319-58771-4_9

Cited by 4 publications

(3 citation statements)

References 27 publications

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“…Regions of uniform curvature, beam divergence, or a combination thereof within an image may be deconvolved by a corresponding CDSF, and then patched together with the rest of the image . This operation has been studied in the case of camera or image blur correction using a spatially variant PSF . This method may have limitations in terms of the number of patches used in the image, or other image processing considerations which are unknown to the authors as yet.…”

Section: Discussionmentioning

confidence: 99%

“…41 This operation has been studied in the case of camera or image blur correction using a spatially variant PSF. [39][40][41][42] This method may have limitations in terms of the number of patches used in the image, or other image processing considerations which are unknown to the authors as yet. It may also be possible to employ more advanced spatially variant deconvolution methods that rely on the assumption of a smoothly varying PSF.…”

Section: Discussionmentioning

confidence: 99%

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A mathematical deconvolution formulation for superficial dose distribution measurement by Cerenkov light dosimetry

Brost

Watanabe

2018

Medical Physics

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

A mathematical deconvolution formulation for superficial dose distribution measurement by Cerenkov light dosimetry

Brost

Watanabe

2018

Medical Physics

View full text Add to dashboard Cite

show abstract

“…More realistic scenarios with both noise and unknown spatially variant blur have not been extensively addressed in the literature. Existing optimizationbased methods require knowledge of the noise level and rely on relatively simple priors assuming a piece-wise constant change of the blur kernel in space [24], [25] or deal with a simplified blur model [26], [27]. The more general spacevariant blur model is well studied in the context of deep learning.…”

Section: A Deblurring In the Presence Of Noisementioning

confidence: 99%

Concurrent Video Denoising and Deblurring for Dynamic Scenes

Katsaros

Ostrowski²,

Węsierski³

et al. 2021

IEEE Access

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Graph-Based Blind Image Deblurring From a Single Photograph

Bai

Cheung

Liu

et al. 2019

IEEE Trans. on Image Process.

157

124

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Blind image deblurring, i.e., deblurring without knowledge of the blur kernel, is a highly ill-posed problem. The problem can be solved in two parts: i) estimate a blur kernel from the blurry image, and ii) given estimated blur kernel, deconvolve blurry input to restore the target image. In this paper, we propose a graph-based blind image deblurring algorithm by interpreting an image patch as a signal on a weighted graph. Specifically, we first argue that a skeleton image-a proxy that retains the strong gradients of the target but smooths out the details-can be used to accurately estimate the blur kernel and has a unique bi-modal edge weight distribution. Then, we design a reweighted graph total variation (RGTV) prior that can efficiently promote a bi-modal edge weight distribution given a blurry patch. Further, to analyze RGTV in the graph frequency domain, we introduce a new weight function to represent RGTV as a graph l1-Laplacian regularizer. This leads to a graph spectral filtering interpretation of the prior with desirable properties, including robustness to noise and blur, strong piecewise smooth (PWS) filtering and sharpness promotion. Minimizing a blind image deblurring objective with RGTV results in a non-convex non-differentiable optimization problem. We leverage the new graph spectral interpretation for RGTV to design an efficient algorithm that solves for the skeleton image and the blur kernel alternately. Specifically for Gaussian blur, we propose a further speedup strategy for blind Gaussian deblurring using accelerated graph spectral filtering. Finally, with the computed blur kernel, recent non-blind image deblurring algorithms can be applied to restore the target image. Experimental results demonstrate that our algorithm successfully restores latent sharp images and outperforms state-of-the-art methods quantitatively and qualitatively.

show abstract

Blind Space-Variant Single-Image Restoration of Defocus Blur

Cited by 4 publications

References 27 publications

A mathematical deconvolution formulation for superficial dose distribution measurement by Cerenkov light dosimetry

A mathematical deconvolution formulation for superficial dose distribution measurement by Cerenkov light dosimetry

Concurrent Video Denoising and Deblurring for Dynamic Scenes

Graph-Based Blind Image Deblurring From a Single Photograph

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