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International Journal of Computer Vision

Erschienen in:

04.09.2023

Blind Image Deblurring with Unknown Kernel Size and Substantial Noise

verfasst von: Zhong Zhuang, Taihui Li, Hengkang Wang, Ju Sun

Erschienen in: International Journal of Computer Vision | Ausgabe 2/2024

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Abstract

Blind image deblurring (BID) has been extensively studied in computer vision and adjacent fields. Modern methods for BID can be grouped into two categories: single-instance methods that deal with individual instances using statistical inference and numerical optimization, and data-driven methods that train deep-learning models to deblur future instances directly. Data-driven methods can be free from the difficulty in deriving accurate blur models, but are fundamentally limited by the diversity and quality of the training data—collecting sufficiently expressive and realistic training data is a standing challenge. In this paper, we focus on single-instance methods that remain competitive and indispensable. However, most such methods do not prescribe how to deal with unknown kernel size and substantial noise, precluding practical deployment. Indeed, we show that several state-of-the-art (SOTA) single-instance methods are unstable when the kernel size is overspecified, and/or the noise level is high. On the positive side, we propose a practical BID method that is stable against both, the first of its kind. Our method builds on the recent ideas of solving inverse problems by integrating physical models and structured deep neural networks, without extra training data. We introduce several crucial modifications to achieve the desired stability. Extensive empirical tests on standard synthetic datasets, as well as real-world NTIRE2020 and RealBlur datasets, show the superior effectiveness and practicality of our BID method compared to SOTA single-instance as well as data-driven methods. The code of our method is available at https://github.com/sun-umn/Blind-Image-Deblurring.

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Indeed, by Young’s convolution inequality and the fact \({\Vert {\varvec{k}} \Vert _1 = 1}\),

https://static-content.springer.com/image/art%3A10.1007%2Fs11263-023-01883-x/MediaObjects/11263_2023_1883_IEq67_HTML.gif

See also similar ideas for the inverse filtering approach in Cabrelli (1985) and Sun and Donoho (2021).

In particular, if they form a measure-zero set.

The result in Eq. (11) assumes a circular convolution model: \(\varvec{y} = \varvec{a} \circledast \varvec{z}\), but it is well known that the linear convolution can be written as circular convolution by appropriate zero-padding to the two convolving components.

We note in passing that the reason we do not use FBC directly is that it may be misleading: the correspondence ratio as they define it can be larger than 1, so in principle the average approaching 1 does not imply that recovery is good. When checking their code (https://github.com/shizenglin/Measure-and-Control-Spectral-Bias), we find that they actually truncate values greater than 1, which potentially make the metric more misleading.

The existing synthetic BID datasets are too small to support training data-driven methods.

Available at https://webee.technion.ac.il/people/anat.levin/papers/LevinEtalCVPR09Data.rar

Available at http://vllab.ucmerced.edu/wlai24/cvpr16_deblur_study/

LAI16 has 4 trajectories to synthesize non-uniform motion blur also, which we do not consider in this paper. Moreover, it also includes 100 real-world blurry images without groundtruth kernels.

Available at (registration needed to download the dataset): https://competitions.codalab.org/competitions/22233#learn_the_details. We suspect that this is a superset of the REDS (REalistic and Dynamic Scenes) dataset (available at https://seungjunnah.github.io/Datasets/reds.html), at least with the same generation procedure as that of REDS.

Available at: http://cg.postech.ac.kr/research/realblur/

NTIRE2020 is developed for data-driven approaches that require an extensive training set.

Code available at: http://cs.brown.edu/~lbsun/deblur2013/deblur2013iccp.html

Code available at: https://jspan.github.io/projects/dark-channel-deblur/index.html

Code available at: https://www.dropbox.com/s/qmxkkwgnmuwrfoj/code_iccv2017_outlier.zip?dl=0

Code available at: https://github.com/lisiyaoATbnu/low_rank_kernel

Code available at: https://github.com/csdwren/SelfDeblur

In DONG17 the loss consists in applying \(h(z) = z^2/2 - \log {(a+e^{bz^2})}/(2b)\) element-wise to \(\varvec{y} - {\varvec{k}} *{\varvec{x}}\), where \(a, b > 0\) and so that \(h(z) \le 0\). Note that \(h(z) \sim O(z^2)\) as \(z \rightarrow 0\), and h(z) approaches the constant 0 when z is large.

SRN is available at: https://github.com/jiangsutx/SRN-Deblur; DeblurGAN-v2 is available at: https://github.com/VITA-Group/DeblurGANv2; ZHANG20 is available at: https://github.com/HDCVLab/Deblurring-by-Realistic-Blurring.

Available at: https://drive.google.com/file/d/1fHkPiZOvLQSc4HhT8-wA6dh0M4skpTMi/view

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Titel: Blind Image Deblurring with Unknown Kernel Size and Substantial Noise
verfasst von: Zhong Zhuang
Taihui Li
Hengkang Wang
Ju Sun
Publikationsdatum: 04.09.2023
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 2/2024
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-023-01883-x

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