Analysis of Radially Restored Images for Spherical Single Lens Cellphone Camera

Zhang, Yupeng; Minema, Ikumi; Zimin, Lev; Ueda, T.

doi:10.1109/jsen.2011.2167504

Cited by 8 publications

(5 citation statements)

References 29 publications

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“…Details of restoration by segmenting a polar image and using multiple PSFs are documented in our previous works. (12,14) The focus of this work is to demonstrate the effectiveness of the PSF filtering technique; thus, multiple PSF restoration by image segmentation is not discussed here. The six steps discussed above can also be used to determine the optimal p for each PSF used in the multiple PSF restoration process.…”

Section: Discussionmentioning

confidence: 99%

“…We proposed a spherical single-element lens imaging system (SSLIS) in our previous research, (12,(14)(15)(16)(17) aiming to reduce the optical complexity and manufacturing costs of a digital camera. This imaging system introduces a radially expanding blur from the center to the four vertices of the image plane because optical aberrations such as field curvature become severe as the field of view (FOV) increases.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, we proposed an image processing method of reducing the blur that uses a polar domain deconvolution algorithm. (12,14) This algorithm converts the recorded image and PSF to a polar coordinate system and carries out 2D deconvolution in the polar domain. The PSF matrix used in the algorithm takes the form of a block Toeplitz matrix with circulant blocks (BTCB) rather than a BTTB because pixels on the left and right boundaries of the converted panoramic polar image are neighbors (see ref.…”

Section: Introductionmentioning

confidence: 99%

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Point Spread Function Filtering for Radially Variant Blur Restoration

Zhang

Zimin²,

Ji³

et al. 2013

Sensors and Materials

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In this paper, we present a point spread function (PSF) filtering technique for solving the radially variant blur restoration problem. Radially variant blur is generated by a spherical single-element lens imaging system (SSLIS) that is embedded in an experimental camera module. The restoration of this category of blur is carried out in a polar coordinate system using polar PSFs at different fields of view (FOVs). However, restoration using large PSFs tends to introduce severe ringing artifacts in the restored image owing to the nonsparse nature of these PSFs. We show in this paper that the PSF filtering technique can effectively minimize ringing artifacts by filtering out some PSF pixels with an intensity lower than the threshold intensity. As a result, a nonsparse PSF becomes a sparse PSF, which is for good restoration results. The effectiveness of the PSF filtering technique was validated by visual comparison using three test images captured by the SSLIS camera module. In addition, a systematic way to determine the optimal filtering coefficient for a PSF at any FOV within the FOV range is also introduced.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Point Spread Function Filtering for Radially Variant Blur Restoration

Zhang

Zimin²,

Ji³

et al. 2013

Sensors and Materials

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“…However, a significant portion of optical theory is developed under the assumption of rotational symmetry; for example, the analysis of an optical system while considering only a single plane is only possible assuming rotational symmetry. The use of rotational symmetry and polar transformations for deblurring arises in modeling motion blur [26,27], suppressing radial variance [28], and segmenting the FoV into shift-invariant radial segments [29][30][31][32]. Additionally, ray models have been developed using Seidel coefficients [33].…”

Section: Related Workmentioning

confidence: 99%

Linear Revolution-Invariance: Modeling and Deblurring Spatially-Varying Imaging Systems

Kohli¹,

Angelopoulos²,

You³

et al. 2022

Preprint

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We develop theory and algorithms for modeling and deblurring imaging systems that are composed of rotationally-symmetric optics. Such systems have point spread functions (PSFs) which are spatiallyvarying, but only vary radially, a property we call linear revolution-invariance (LRI). From the LRI property we develop an exact theory for linear imaging with radially-varying optics, including an analog of the Fourier Convolution Theorem. This theory, in tandem with a calibration procedure using Seidel aberration coefficients, yields an efficient forward model and deblurring algorithm which requires only a single calibration image-one that is easier to measure than a single PSF. We test these methods in simulation and experimentally on images of resolution targets, rabbit liver tissue, and live tardigrades obtained using the UCLA Miniscope v3. We find that the LRI forward model generates accurate radiallyvarying blur, and LRI deblurring improves resolution, especially near the edges of the field-of-view.

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“…The blurred polar images were segmented into different numbers of locally invariant regions and restored by applying different numbers of polar PSFs [19]. The image restoration effects on an apartment building, car and observing tower are shown in Fig.…”

Section: Single Lens Camera With Optical Aberrationmentioning

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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Analysis of Radially Restored Images for Spherical Single Lens Cellphone Camera

Cited by 8 publications

References 29 publications

Point Spread Function Filtering for Radially Variant Blur Restoration

Point Spread Function Filtering for Radially Variant Blur Restoration

Linear Revolution-Invariance: Modeling and Deblurring Spatially-Varying Imaging Systems

Parameterized Modeling of Spatially Varying PSF for Lens Aberration and Defocus

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