A Logarithmic Image Prior for Blind Deconvolution

Blurring occurs frequently in video sequences captured by consumer devices, as a result of various factors such as lens aberrations, defocus, relative camerascene motion, and camera shake. When it comes to the contents of archive documents such as old films and television shows, the degradations are even more serious due to several physical phenomena happening during the sensing, transmission, recording, and storing processes. We propose in this paper a versatile formulation of blind video deconvolution problems that seeks to estimate both the sharp unknown video sequence and the underlying blur kernel from an observed video. This inverse problem is ill-posed, and an appropriate solution can be obtained by modeling it as a nonconvex minimization problem. We provide a novel iterative algorithm to solve it, grounded on the use of recent advances in convex and nonconvex optimization techniques, and having the ability of including numerous well-known regularization strategies.

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“…• Smoothed log-Total Variation (log-TV) is a nonconvex smooth sparsity promoting regularization function from [27] defined as follows…”

Section: Video Estimationmentioning

confidence: 99%

An alternating proximal approach for blind video deconvolution

Abboud¹,

Chouzenoux

Pesquet

et al. 2019

Signal Processing: Image Communication

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“…k 2 << n 2 ), n 2 observations should be sufficient to obtain a good kernel estimate. Based on the fact, many authors [13,14,15] also used the flat prior on the kernel, enforcing only its non-negativity and normalization constraints.…”

Section: Kernel Priormentioning

confidence: 99%

“…m ) according to (16) [14] , (Bottom Left) Perrone et al [15], and (Bottom Right) the proposed method with the same non-blind deblurring algorithm [23].…”

Section: Inference Algorithmmentioning

confidence: 99%

“…Moreover, Wipf and Zhang [13] emphasize the power of image prior that can discriminate a good sharp image from blurred images is critical to recovering the unknown image. Both Babacan [14] and Perrone [15] are good examples of this approach. They [13,14,15] all utilized non-convex image priors to strongly promote the sparsity of the signal, regardless of the natural statistics, and achieved state-of-the-art blind image deconvolution result.…”

Section: Introductionmentioning

confidence: 99%

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Blind image deconvolution using Student's-t prior with overlapping group sparsity

Jeon

Kang

Yoo

2017

2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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In this paper, we solve blind image deconvolution problem that is to remove blurs form a signal degraded image without any knowledge of the blur kernel. Since the problem is ill-posed, an image prior plays a significant role in accurate blind deconvolution. Traditional image prior assumes coefficients in filtered domains are sparse. However, it is assumed here that there exist additional structures over the sparse coefficients. Accordingly, we propose new problem formulation for the blind image deconvolution, which utilize the structural information by coupling Student's-t image prior with overlapping group sparsity. The proposed method resulted in an effective blind deconvolution algorithm that outperforms other state-of-the-art algorithms.

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“…Single image deblurring is a classical problem in image processing communities. A few image debluring methods [1][2][3][4][5][6][7] may be the most representatively used to handle deblurring problems. The work of Lai et al [8] provided an overview of a series of deblurring methods [9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Blind Deconvolution with Scale Ambiguity

Fan

Wang

et al. 2020

Applied Sciences

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Recent years have witnessed significant advances in single image deblurring due to the increasing popularity of electronic imaging equipment. Most existing blind image deblurring algorithms focus on designing distinctive image priors for blur kernel estimation, which usually play regularization roles in deconvolution formulation. However, little research effort has been devoted to the relative scale ambiguity between the latent image and the blur kernel. The well-known L 1 normalization constraint, i.e., fixing the sum of all the kernel weights to be one, is commonly selected to remove this ambiguity. In contrast to this arbitrary choice, we in this paper introduce the L p -norm normalization constraint on the blur kernel associated with a hyper-Laplacian prior. We show that the employed hyper-Laplacian regularizer can be transformed into a joint regularized prior based on a scale factor. We quantitatively show that the proper choice of p makes the joint prior sufficient to favor the sharp solutions over the trivial solutions (the blurred input and the delta kernel). This facilitates the kernel estimation within the conventional maximum a posterior (MAP) framework. We carry out numerical experiments on several synthesized datasets and find that the proposed method with p = 2 generates the highest average kernel similarity, the highest average PSNR and the lowest average error ratio. Based on these numerical results, we set p = 2 in our experiments. The evaluation on some real blurred images demonstrate that the results by the proposed methods are visually better than the state-of-the-art deblurring methods.

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A Logarithmic Image Prior for Blind Deconvolution

Cited by 42 publications

References 33 publications

An alternating proximal approach for blind video deconvolution

An alternating proximal approach for blind video deconvolution

Blind image deconvolution using Student's-t prior with overlapping group sparsity

Blind Deconvolution with Scale Ambiguity

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