Microscope with extension of the depth of field by employing a cubic phase plate on the surface of lens

Lee, Chi‐Feng; Lee, Cheng-Chung

doi:10.1016/j.rio.2021.100107

Cited by 5 publications

(7 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is also the first demonstration of an extended depth of field Deep Lens with a mobile device form factor. Due to the more powerful design space enabled by our method, the result achieves larger extended depth range (20 cm to 10 m) and a larger field of view (57.3 • ) compared to prior work [23,26,14]. Our results point to the feasibility of introducing extended depth of field capabilities into future mobile devices.…”

Section: Discussionmentioning

confidence: 71%

“…Extended depth of field computational cameras seek to combine the light sensitivity of large apertures with a large depth of field. Wavefront coding [9,5,33,14] introduces an odd-polynomial plate into an optical system, which has the effect of uniformly blurring the image in a focus-independent fashion. A sharp image is then computationally reconstructed by deconvolution of this uniformly blurred raw camera image.…”

Section: Deep Lens For Extended Depth-of-field Imaging With Mobile De...mentioning

confidence: 99%

See 1 more Smart Citation

Curriculum Learning for ab initio Deep Learned Refractive Optics

Yang¹,

Fu²,

Heidrich³

2023

Preprint

View full text Add to dashboard Cite

Deep lens optimization has recently emerged as a new paradigm for designing computational imaging systems, however it has been limited to either simple optical systems consisting of a single element such as a diffractive optical element (DOE) or metalens, or the fine-tuning of compound lenses from good initial designs. Here we present a deep lens design method based on curriculum learning, which is able to learn optical designs of compound lenses ab initio from randomly initialized surfaces without human intervention, therefore overcoming the need for a good initial design. We demonstrate this approach with the fullyautomatic design of an extended depth-of-field computational camera in a cellphone-style form factor, highly aspherical surfaces, and a short back focal length.

show abstract

Section: Discussionmentioning

confidence: 71%

Section: Deep Lens For Extended Depth-of-field Imaging With Mobile De...mentioning

confidence: 99%

Curriculum Learning for ab initio Deep Learned Refractive Optics

Yang¹,

Fu²,

Heidrich³

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Second, wavefront coding is based on the imperfect paraxial approximation, which assumes that optical system is shift-invariant. Therefore, many wavefront coding systems, mainly small FOV systems, only consider the defocus consistency of the points on the optical axis [78][79][80][81][82][83][84] . However, the actual optical system does not conform well to shift-invariance.…”

Section: ( ) ( ) Exp J J ( )mentioning

confidence: 99%

“…While non-circularly symmetric phase masks can better suppress aberration 76 . Phase masks can either be placed at the aperture stop of the imaging system or integrated on the surface of the optics 80,[92][93][94][95] .…”

Section: Phase Mask and Reconstruction Algorithmmentioning

confidence: 99%

“…However, this method ignores aberrations and is therefore only suitable for optical systems with a small aberration such as microscopes. For imaging systems with non-negligible aberrations, PSFs/OTFs needs to be calculated based on optical design software 68,80,85,86,[99][100][101] . This way can take actual aberrations into account and achieve better imaging quality, but requires specific design and lacks flexibility.…”

Section: Phase Mask and Reconstruction Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Point spread function engineering based on wavefront phase modulation

Zhang,

Cui,

Yue

et al. 2023

Optoelectronic Imaging and Multimedia Technology X

View full text Add to dashboard Cite

Curriculum learning for ab initio deep learned refractive optics

Yang,

Fu,

Heidrich

2024

Nat Commun

View full text Add to dashboard Cite

Deep optical optimization has recently emerged as a new paradigm for designing computational imaging systems using only the output image as the objective. However, it has been limited to either simple optical systems consisting of a single element such as a diffractive optical element or metalens, or the fine-tuning of compound lenses from good initial designs. Here we present a DeepLens design method based on curriculum learning, which is able to learn optical designs of compound lenses ab initio from randomly initialized surfaces without human intervention, therefore overcoming the need for a good initial design. We demonstrate the effectiveness of our approach by fully automatically designing both classical imaging lenses and a large field-of-view extended depth-of-field computational lens in a cellphone-style form factor, with highly aspheric surfaces and a short back focal length.

show abstract

Microscope with extension of the depth of field by employing a cubic phase plate on the surface of lens

Cited by 5 publications

References 14 publications

Curriculum Learning for ab initio Deep Learned Refractive Optics

Curriculum Learning for ab initio Deep Learned Refractive Optics

Point spread function engineering based on wavefront phase modulation

Curriculum learning for ab initio deep learned refractive optics

Contact Info

Product

Resources

About