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3D Lensless Imaging : = Theory, Hardware, and Algorithms = San wei wu jing cheng xiang : li lun, ying jian, han suan fa.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	3D Lensless Imaging :/
Reminder of title:	Theory, Hardware, and Algorithms = San wei wu jing cheng xiang : li lun, ying jian, han suan fa.
remainder title:	San wei wu jing cheng xiang :
Author:	Hua, Yi.
Description:	1 online resource (118 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 84-08, Section: B.
Contained By:	Dissertations Abstracts International84-08B.
Subject:	Electrical engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798374410761

3D Lensless Imaging : = Theory, Hardware, and Algorithms = San wei wu jing cheng xiang : li lun, ying jian, han suan fa.
Hua, Yi.

3D Lensless Imaging :Theory, Hardware, and Algorithms = San wei wu jing cheng xiang : li lun, ying jian, han suan fa.San wei wu jing cheng xiang :li lun, ying jian, han suan fa. - 1 online resource (118 pages)

Source: Dissertations Abstracts International, Volume: 84-08, Section: B.

Thesis (Ph.D.)--Carnegie Mellon University, 2023.

Includes bibliographical references

Lensless cameras enable us to see in many challenging scenarios. They can image in narrow spaces where lenses do not fit, operate at wavelengths where lenses do not work, and make ultra-wide field-of- view microscopes. Despite their novel capabilities, current lensless cameras have limited imaging quality that restricts their practicality. These limitations can be attributed to the conditioning and complexity of the inverse problem that lensless imagers must solve to obtain the scene.A common design in lensless imaging is that of a thin attenuating mask placed before a sensor. For a scene restricted to a front-parallel plane, the image formation model can be approximated as a 2D convolution between the plane's texture and a scaled version of the mask pattern, and the ensuing inverse problem has efficient solutions. However, scenes of more complex geometry, such as those spanning a large depth range, pose a difficult and under-determined inverse problem. This thesis aims to develop lensless imaging techniques to effectively and efficiently photograph 3D scenes with an extended depth range. To that end, we make the following contributions to the theory, hardware, and algorithms of 3D lensless imaging.First, we present a theoretical analysis of the spatial and axial resolution limits of a mask-based lens- less camera, which provides an understanding of the performance of various camera designs. Specifically, we derive the closed-form expression of a 3D modulation transfer function as a function of the mask pattern, and connect the parameters of the mask to the camera's achievable spatio-axial resolution.Second, we introduce programmable masks in lensless imagers to increase the number of measurements by capturing multiple frames while displaying different mask patterns. This upgrade in hardware allows computational focusing at a given depth, such that the resulting measurements are well approximated as a result of 2D convolution, even when the scene extends over a large depth range. As a result, the texture corresponding to a specific depth can be recovered with an efficient deconvolution method with fewer artifacts.Finally, we present an inverse rendering approach to the reconstruction problem, which requires a joint solution of the texture and shape of the scene. This approach solves the inverse problem under a physically realistic and differentiable forward model. It allows us to faithfully represent scenes as surfaces instead of volumetric albedo functions as is commonly used in previous works, and avoids reconstruction artifacts arising from model mismatch.Together, those three contributions provide a fundamental advance to 3D lensless imaging.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798374410761Subjects--Topical Terms:

596380
Electrical engineering.
Subjects--Index Terms:

3D imagingIndex Terms--Genre/Form:

554714
Electronic books.

3D Lensless Imaging : = Theory, Hardware, and Algorithms = San wei wu jing cheng xiang : li lun, ying jian, han suan fa.
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520 $a Lensless cameras enable us to see in many challenging scenarios. They can image in narrow spaces where lenses do not fit, operate at wavelengths where lenses do not work, and make ultra-wide field-of- view microscopes. Despite their novel capabilities, current lensless cameras have limited imaging quality that restricts their practicality. These limitations can be attributed to the conditioning and complexity of the inverse problem that lensless imagers must solve to obtain the scene.A common design in lensless imaging is that of a thin attenuating mask placed before a sensor. For a scene restricted to a front-parallel plane, the image formation model can be approximated as a 2D convolution between the plane's texture and a scaled version of the mask pattern, and the ensuing inverse problem has efficient solutions. However, scenes of more complex geometry, such as those spanning a large depth range, pose a difficult and under-determined inverse problem. This thesis aims to develop lensless imaging techniques to effectively and efficiently photograph 3D scenes with an extended depth range. To that end, we make the following contributions to the theory, hardware, and algorithms of 3D lensless imaging.First, we present a theoretical analysis of the spatial and axial resolution limits of a mask-based lens- less camera, which provides an understanding of the performance of various camera designs. Specifically, we derive the closed-form expression of a 3D modulation transfer function as a function of the mask pattern, and connect the parameters of the mask to the camera's achievable spatio-axial resolution.Second, we introduce programmable masks in lensless imagers to increase the number of measurements by capturing multiple frames while displaying different mask patterns. This upgrade in hardware allows computational focusing at a given depth, such that the resulting measurements are well approximated as a result of 2D convolution, even when the scene extends over a large depth range. As a result, the texture corresponding to a specific depth can be recovered with an efficient deconvolution method with fewer artifacts.Finally, we present an inverse rendering approach to the reconstruction problem, which requires a joint solution of the texture and shape of the scene. This approach solves the inverse problem under a physically realistic and differentiable forward model. It allows us to faithfully represent scenes as surfaces instead of volumetric albedo functions as is commonly used in previous works, and avoids reconstruction artifacts arising from model mismatch.Together, those three contributions provide a fundamental advance to 3D lensless imaging.
520 $a 无镜头相机使我们能够看到许多难以捕捉的场景。这些相机可以在放不下镜头的狭窄空间拍照，在镜头不合适的波长下拍照，也能作为超宽视场的显微镜。尽管具有新颖的功能，但当前的无镜头相机的成像质量有限，这限制了它们的实用性。目前成像质量的限制来源于无镜头相机为了计算它所拍摄的场景需要解决的逆问题的条件数和复杂性。无镜成像中的一个常见设计是在传感器之前放置一个薄的衰减掩模。当场景中仅有一个平行于相机传感器的平面时，相机的成像模型可以近似为平面的纹理与缩放的掩模图案之间的二维卷积，它的逆问题具有快速的求解方法。然而，在具有更复杂几何形状的场景，例如跨越大深度范围的三维场景，会带来困难且欠定的反演问题。本论文旨在开发无镜头成像技术，以高质量并快速地重建具有大深度范围的三维场景。为此，我们对三维无镜成像的理论、硬件和算法做出了以下贡献。首先，我们对基于掩模的无镜头相机的空间和轴向分辨率限制进行了理论分析，这可以让我们理解和比较各种不同的相机设计的性能。具体来说，我们推导出用掩模图案表示的三维调制传递函数的解析表达式，并将掩模的参数与相机可实现的空间及轴向分辨率联系起来。其次，我们在无镜头相机中引入了可编程掩模，通过在显示不同掩模图案的同时捕获多个帧来增加测量次数。这种硬件升级让我们可以在给定的深度进行计算聚焦，这样即使场景有很大的深度范围，测量结果也可以很好地近似为二维卷积的结果。因此，特定深度上的纹理可以使用高效的反卷积方法来恢复，但避免结果中过多的高频伪影。最后，我们提出一种逆向渲染的方法来解决场景重建的问题，包括对场景的纹理和形状的估计。提出的方法在较接近物理现实的且可导的成像模型下解决你问题。提出的方法使我们能够忠实地将场景表示为表面，而不是以前作品中常用的体积反照率函数，并避免因模型不匹配而产生的重建伪影。这三项贡献共同为三维无镜成像提供了根本性的进步。
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Electrical engineering. $3 596380
653 $a 3D imaging
653 $a Computational imaging
653 $a Lensless imaging
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710 2 $a Carnegie Mellon University. $b Electrical and Computer Engineering. $3 1182305
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880 1 0 $6 245-01/$1 $a 3D Lensless Imaging : $b Theory, Hardware, and Algorithms = 三维无镜成像 : 理论, 硬件, 和算法.
880 3 1 $6 246-02/$1 $a 三维无镜成像 : $b 理论, 硬件, 和算法.