Modeling and removing spatially-varying optical blur

Kee, Eric; Paris, Sylvain; Chen, Simon; Wang, Jue

doi:10.1109/iccphot.2011.5753120

Cited by 68 publications

(71 citation statements)

References 21 publications

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“…In this paper, we use the two dimensional NSAS distribution Blur of a set of single focal length lenses at various pixel locations in a sensor at different aperture values is estimated and fitted by the proposed parametric blur models. The accuracy of the proposed models are evaluated and compared to other parametric lens blur models [3], [9] and other bivariate distributions that address skewness [12], [16]. The advantage of using the proposed models is demonstrated with deblurring experiments.…”

Section: List Of Tablesmentioning

confidence: 99%

“…Modern optical systems correct distortions through the arrangement of optical elements and through the use of sophisticated optical elements such as aspherical and extra low dispersion lenses [1], [2]. It is reported that images captured even with a sophisticated lens system under ideal conditions without camera shake or motion still show degradation [3], [4], [5], [6], [7], [8], [9], [10], [11]. The blur in images is non-stationary in a sense the amount of blur depends on the pixel locations in a sensor.…”

Section: List Of Tablesmentioning

confidence: 99%

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Modeling Non-Stationary Asymmetric Lens Blur by Normal Sinh-Arcsinh Model

Jang

Yun

Yang

2016

IEEE Trans. on Image Process.

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Images acquired by a camera show lens blur due to imperfection in the optical system. Lens blur is non-stationary in a sense the amount of blur depends on pixel locations in a sensor. Lens blur is also asymmetric in a sense the amount of blur is different in the radial and tangential directions, and also in the inward and outward radial directions. This paper presents parametric blur kernel models based on the normal sinh-arcsinh distribution function. The proposed models can provide flexible shapes of blur kernels with different symmetry and skewness to model complicated lens blur accurately. Blur of single focal length lenses is estimated and the accuracy of the models is compared with existing parametric blur models. Advantage of the proposed models is demonstrated through deblurring experiments.6

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Section: List Of Tablesmentioning

confidence: 99%

Section: List Of Tablesmentioning

confidence: 99%

Modeling Non-Stationary Asymmetric Lens Blur by Normal Sinh-Arcsinh Model

Jang

Yun

Yang

2016

IEEE Trans. on Image Process.

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“…Another approach to SV BD is to parameterize kernels with a small number of parameters, assuming specific blur types, e.g., motion blur [6], [11], [19]- [23] or defocus blur [15], [24]- [27]. A drawback of such methods is that they can only handle targeted blur types.…”

Section: Shift-invariant and Variant Blind Deconvolutionmentioning

confidence: 99%

Shift-Variant Blind Deconvolution Using a Field of Kernels

Sonogashira

Iiyama

Minoh

2017

IEICE Trans. Inf. & Syst.

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Motoharu SONOGASHIRA†a) , Nonmember, Masaaki IIYAMA †b) , Member, and Michihiko MINOH †c) , Fellow SUMMARY Blind deconvolution (BD) is the problem of restoring sharp images from blurry images when convolution kernels are unknown. While it has a wide range of applications and has been extensively studied, traditional shift-invariant (SI) BD focuses on uniform blur caused by kernels that do not spatially vary. However, real blur caused by factors such as motion and defocus is often nonuniform and thus beyond the ability of SI BD. Although specialized methods exist for nonuniform blur, they can only handle specific blur types. Consequently, the applicability of BD for general blur remains limited. This paper proposes a shift-variant (SV) BD method that models nonuniform blur using a field of kernels that assigns a local kernel to each pixel, thereby allowing pixelwise variation. This concept is realized as a Bayesian model that involves SV convolution with the field of kernels and smoothing of the field for regularization. A variationalBayesian inference algorithm is derived to jointly estimate a sharp latent image and a field of kernels from a blurry observed image. Owing to the flexibility of the field-of-kernels model, the proposed method can deal with a wider range of blur than previous approaches. Experiments using images with nonuniform blur demonstrate the effectiveness of the proposed SV BD method in comparison with previous SI and SV approaches. key words: blind deconvolution, deblurring, shift-variant, variational Bayes IntroductionBlind deconvolution (BD) for deblurring is one of the most extensively studied topics in image processing [1], [2]. Essentially, blurring of images is modeled as convolution of images and kernels. Deconvolution is the inverse of this process, which effectively restores sharp images from blurry ones. The objective of BD is to perform deconvolution when kernels are unknown, which is often the case in practice [1]. Since undesirable blur is quite common in the real world, BD has a wide range of applications, e.g., mobile photography [3], computational photography [4], computer vision [5], astronomical imaging [6], and biomedical imaging [7].Traditional shift-invariant (SI) BD assumes uniform blur produced by kernels that do not vary spatially across images, such as the example in Fig. 1 (b). However, in reality blur kernels often spatially vary, producing nonuni- . For example, independently moving objects produce different effects of motion blur, and objects distant from a camera are more subject to defocus blur than close objects. Blur is often more complex since both motion and defocus blur can simultaneously occur, as shown in Fig. 1 (c). In such cases, SI BD methods fail in kernel estimation and also in deconvolution [1]. Although specialized BD methods for nonuniform blur have been recently developed, they can only handle certain types of blur, e.g., motion, defocus, or locally uniform blur [2]. Consequently, the applicability of BD to general blurry images remains limited.I...

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“…(See [1,3,4,6,7,9,10,13,14] and references therein.) Joshi et al [3] estimate PSFs from edges in the image.…”

Section: Related Workmentioning

confidence: 99%

“…The PSF can present spatially varying blur, which lacks a parameterized model. E. Kee et al [6] introduce a method to estimate the blur kernel densely over the image and across multiple aperture and zoom settings. Simpkins and Stevenson [1,7,10,13] have developed methods using geometrical optics to construct a parameterized model for a spatially varying PSF due to lens aberrations and defocus.…”

Section: Related Workmentioning

confidence: 99%

Parameterized Modeling of Spatially Varying PSF for Lens Aberration and Defocus

Wang

Chen

Jia

et al. 2015

Journal of the Optical Society of Korea

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Image deblurring by a deconvolution method requires accurate knowledge of the blur kernel. Existing point spread function (PSF) models in the literature corresponding to lens aberrations and defocus are either parameterized and spatially invariant or spatially varying but discretely defined. In this paper, a parameterized model is developed and presented for a PSF which is spatially varying due to lens aberrations and defocus in an imaging system. The model is established from the Seidel third-order aberration coefficient and the Hu moment. A skew normal Gauss model is selected for parameterized PSF geometry structure. The accuracy of the model is demonstrated with simulations and measurements for a defocused infrared camera and a single spherical lens digital camera. Compared with optical software Code V, the visual results of two optical systems validate our analysis and proposed method in size, shape and direction. Quantitative evaluation results reveal the excellent accuracy of the blur kernel model.

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Modeling and removing spatially-varying optical blur

Cited by 68 publications

References 21 publications

Modeling Non-Stationary Asymmetric Lens Blur by Normal Sinh-Arcsinh Model

Modeling Non-Stationary Asymmetric Lens Blur by Normal Sinh-Arcsinh Model

Shift-Variant Blind Deconvolution Using a Field of Kernels

Parameterized Modeling of Spatially Varying PSF for Lens Aberration and Defocus

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