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Article ; Online: Generalization of learned Fourier-based phase-diversity wavefront sensing.

Zhou, Zhisheng / Fu, Qiang / Zhang, Jingang / Nie, Yunfeng

2023 Volume 31, Issue 7, Page(s) 11729–11744

Abstract: Proper initialization of the nonlinear optimization is important to avoid local minima in phase diversity wavefront sensing (PDWS). An effective neural network based on low-frequency coefficients in the Fourier domain has proved effective to determine a ... ...

Abstract	Proper initialization of the nonlinear optimization is important to avoid local minima in phase diversity wavefront sensing (PDWS). An effective neural network based on low-frequency coefficients in the Fourier domain has proved effective to determine a better estimate of the unknown aberrations. However, the network relies significantly on the training settings, such as imaging object and optical system parameters, resulting in a weak generalization ability. Here we propose a generalized Fourier-based PDWS method by combining an object-independent network with a system-independent image processing procedure. We demonstrate that a network trained with a specific setting can be applied to any image regardless of the actual settings. Experimental results show that a network trained with one setting can be applied to images with four other settings. For 1000 aberrations with RMS wavefront errors bounded within [0.2 λ, 0.4 λ], the mean RMS residual errors are 0.032 λ, 0.039 λ, 0.035 λ, and 0.037 λ, respectively, and 98.9% of the RMS residual errors are less than 0.05 λ.
Language	English
Publishing date	2023-05-08
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.484057
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Correcting Optical Aberration via Depth-Aware Point Spread Functions.

Luo, Jun / Nie, Yunfeng / Ren, Wenqi / Cao, Xiaochun / Yang, Ming-Hsuan

IEEE transactions on pattern analysis and machine intelligence

2024 Volume PP

Abstract: Optical aberration is a ubiquitous degeneration in realistic lens-based imaging systems. Optical aberrations are caused by the differences in the optical path length when light travels through different regions of the camera lens with different incident ... ...

Abstract	Optical aberration is a ubiquitous degeneration in realistic lens-based imaging systems. Optical aberrations are caused by the differences in the optical path length when light travels through different regions of the camera lens with different incident angles. The blur and chromatic aberrations manifest significant discrepancies when the optical system changes. This work designs a transferable and effective image simulation system of simple lenses via multi-wavelength, depth-aware, spatially-variant four-dimensional point spread functions (4D-PSFs) estimation by changing a small amount of lens-dependent parameters. The image simulation system can alleviate the overhead of dataset collecting and exploiting the principle of computational imaging for effective optical aberration correction. With the guidance of domain knowledge about the image formation model provided by the 4D-PSFs, we establish a multi-scale optical aberration correction network for degraded image reconstruction, which consists of a scene depth estimation branch and an image restoration branch. Specifically, we propose to predict adaptive filters with the depth-aware PSFs and carry out dynamic convolutions, which facilitate the model's generalization in various scenes. We also employ convolution and self-attention mechanisms for global and local feature extraction and realize a spatially-variant restoration. The multi-scale feature extraction complements the features across different scales and provides fine details and contextual features. Extensive experiments demonstrate that our proposed algorithm performs favorably against state-of-the-art restoration methods. The source code and trained models are available to the public.
Language	English
Publishing date	2024-02-27
Publishing country	United States
Document type	Journal Article
ISSN	1939-3539
ISSN (online)	1939-3539
DOI	10.1109/TPAMI.2024.3370794
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Multi-scale, multi-dimensional binocular endoscopic image depth estimation network.

Wang, Xiongzhi / Nie, Yunfeng / Ren, Wenqi / Wei, Min / Zhang, Jingang

Computers in biology and medicine

2023 Volume 164, Page(s) 107305

Abstract: During invasive surgery, the use of deep learning techniques to acquire depth information from lesion sites in real-time is hindered by the lack of endoscopic environmental datasets. This work aims to develop a high-accuracy three-dimensional (3D) ... ...

Abstract	During invasive surgery, the use of deep learning techniques to acquire depth information from lesion sites in real-time is hindered by the lack of endoscopic environmental datasets. This work aims to develop a high-accuracy three-dimensional (3D) simulation model for generating image datasets and acquiring depth information in real-time. Here, we proposed an end-to-end multi-scale supervisory depth estimation network (MMDENet) model for the depth estimation of pairs of binocular images. The proposed MMDENet highlights a multi-scale feature extraction module incorporating contextual information to enhance the correspondence precision of poorly exposed regions. A multi-dimensional information-guidance refinement module is also proposed to refine the initial coarse disparity map. Statistical experimentation demonstrated a 3.14% reduction in endpoint error compared to state-of-the-art methods. With a processing time of approximately 30fps, satisfying the requirements of real-time operation applications. In order to validate the performance of the trained MMDENet in actual endoscopic images, we conduct both qualitative and quantitative analysis with 93.38% high precision, which holds great promise for applications in surgical navigation.
MeSH term(s)	Endoscopy ; Computer Simulation ; Surgery, Computer-Assisted
Language	English
Publishing date	2023-08-01
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2023.107305
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Article ; Online: Freeform optical system design with differentiable three-dimensional ray tracing and unsupervised learning.

Nie, Yunfeng / Zhang, Jingang / Su, Runmu / Ottevaere, Heidi

Optics express

2022 Volume 31, Issue 5, Page(s) 7450–7465

Abstract: Optical systems have been crucial for versatile applications such as consumer electronics, remote sensing and biomedical imaging. Designing optical systems has been a highly professional work due to complicated aberration theories and intangible rules-of- ...

Abstract	Optical systems have been crucial for versatile applications such as consumer electronics, remote sensing and biomedical imaging. Designing optical systems has been a highly professional work due to complicated aberration theories and intangible rules-of-thumb, hence neural networks are only coming into this realm until recent years. In this work, we propose and implement a generic, differentiable freeform raytracing module, suitable for off-axis, multiple-surface freeform/aspheric optical systems, paving the way toward a deep learning-based optical design method. The network is trained with minimal prior knowledge, and it can infer numerous optical systems after a one-time training. The presented work unlocks great potential for deep learning in various freeform/aspheric optical systems, and the trained network could serve as an effective, unified platform for generating, recording, and replicating good initial optical designs.
Language	English
Publishing date	2022-11-03
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.484531
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Phase-diversity wavefront sensing enhanced by a Fourier-based neural network.

Zhou, Zhisheng / Zhang, Jingang / Fu, Qiang / Nie, Yunfeng

Optics express

2022 Volume 30, Issue 19, Page(s) 34396–34410

Abstract: Phase diversity wavefront sensing (PDWS) has been a successful approach to quantifying wavefront aberrations with only a few intensity measurements and nonlinear optimization. However, the inherent non-convexity of the inverse problem may lead to ... ...

Abstract	Phase diversity wavefront sensing (PDWS) has been a successful approach to quantifying wavefront aberrations with only a few intensity measurements and nonlinear optimization. However, the inherent non-convexity of the inverse problem may lead to stagnation at a local minimum far from the true solution. Proper initialization of the nonlinear optimization is important to avoid local minima and improve wavefront retrieval accuracy. In this paper, we propose an effective neural network based on low-frequency coefficients in the Fourier domain to determine a better estimate of the unknown aberrations. By virtue of the proposed network, only a small amount of simulation data suffice for a robust training, two orders of magnitude less than those in existing work. Experimental results show that, when compared with some existing methods, our method achieves the highest accuracy while drastically reducing the training time to 1.4 min. The minimum, maximum, and mean values of the root mean square (RMS) residual errors for 800 aberrations are 0.017λ, 0.056λ, and 0.039λ, respectively, and 95% of the RMS residual errors are less than 0.05λ.
Language	English
Publishing date	2022-10-14
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.466292
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Image Quality Is Not All You Want

Yang, Xinge / Fu, Qiang / Nie, Yunfeng / Heidrich, Wolfgang

Task-Driven Lens Design for Image Classification

2023

Abstract: In computer vision, it has long been taken for granted that high-quality images obtained through well-designed camera lenses would lead to superior results. However, we find that this common perception is not a "one-size-fits-all" solution for diverse ... ...

Abstract	In computer vision, it has long been taken for granted that high-quality images obtained through well-designed camera lenses would lead to superior results. However, we find that this common perception is not a "one-size-fits-all" solution for diverse computer vision tasks. We demonstrate that task-driven and deep-learned simple optics can actually deliver better visual task performance. The Task-Driven lens design approach, which relies solely on a well-trained network model for supervision, is proven to be capable of designing lenses from scratch. Experimental results demonstrate the designed image classification lens (``TaskLens'') exhibits higher accuracy compared to conventional imaging-driven lenses, even with fewer lens elements. Furthermore, we show that our TaskLens is compatible with various network models while maintaining enhanced classification accuracy. We propose that TaskLens holds significant potential, particularly when physical dimensions and cost are severely constrained. Comment: Use an image classification network to supervise the lens design from scratch. The final designs can achieve higher accuracy with fewer optical elements
Keywords	Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Graphics ; Physics - Optics
Subject code	006
Publishing date	2023-05-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Quantitatively distinguishing the factors driving sediment flux variations in the Daling River Basin, North China

Sun, Shuang / Zhu, Liya / Hu, Ke / Li, Yan / Nie, Yunfeng

Catena. 2022 May, v. 212

2022

Abstract: Understanding the riverine sediment load regime is crucial to sustainably managing fluvial-deltaic ecosystem restoration. However, in studies on global river sediment transport characteristics, small mountainous rivers (SMRs), transporting 45% of the ... ...

Abstract	Understanding the riverine sediment load regime is crucial to sustainably managing fluvial-deltaic ecosystem restoration. However, in studies on global river sediment transport characteristics, small mountainous rivers (SMRs), transporting 45% of the global sediment flux into the sea, have been largely ignored. Furthermore, SMRs have hydrologic characteristics of instantaneous enormous fluxes under extreme climate, but the research on their driving factors affecting sediment transport is insufficient. Partial derivation method and multiple double mass curves were applied to a small mountainous river in North China, the Daling River to study how the three most variable factors—precipitation, vegetation coverage, and reservoirs—have quantitatively controlled sediment flux in the basin and its subregions in the last 50 years. The results of the two methods showed that the 78.55% reduction in sediment flux from 1961–1979 to 1980–2015 was due primarily to restored vegetation coverage (50.42%-61.11%), followed by reservoirs (18.13–44.09%) and precipitation (5.49–22.34%) in the Daling River Basin (DRB) and its subregions. The restored vegetation in the DRB has intercepted a large amount of sediment, reducing the sediment flux transported to the reservoirs. Besides, the Daling River releases enormous amounts of sediment during heavy rainfalls. However, increasing vegetation coverage in recent years has changed the transport characteristics of sediment into temperate changes in runoff and sediment flux under extreme climatic conditions; thus this river provides a useful example of studying the characteristics of sediment transport controlled by nature and human activities against extreme climate conditions. In the DRB, topography, lithology, and vegetation density are the motivators for the differences in sediment flux and its change rates, from upstream to downstream. These findings emphasize the crucial role of vegetation in driving sediment transport from source to sink in temperate SMRs.
Keywords	basins ; catenas ; ecological restoration ; humans ; lithology ; mountains ; riparian areas ; rivers ; runoff ; sediment contamination ; sediment transport ; sediments ; topography ; vegetation ; watersheds ; China
Language	English
Dates of publication	2022-05
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	519608-5
ISSN	1872-6887 ; 0008-7769 ; 0341-8162
ISSN (online)	1872-6887 ; 0008-7769
ISSN	0341-8162
DOI	10.1016/j.catena.2022.106094
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A survey on computational spectral reconstruction methods from RGB to hyperspectral imaging.

Zhang, Jingang / Su, Runmu / Fu, Qiang / Ren, Wenqi / Heide, Felix / Nie, Yunfeng

Scientific reports

2022 Volume 12, Issue 1, Page(s) 11905

Abstract: Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically ... ...

Abstract	Hyperspectral imaging enables many versatile applications for its competence in capturing abundant spatial and spectral information, which is crucial for identifying substances. However, the devices for acquiring hyperspectral images are typically expensive and very complicated, hindering the promotion of their application in consumer electronics, such as daily food inspection and point-of-care medical screening, etc. Recently, many computational spectral imaging methods have been proposed by directly reconstructing the hyperspectral information from widely available RGB images. These reconstruction methods can exclude the usage of burdensome spectral camera hardware while keeping a high spectral resolution and imaging performance. We present a thorough investigation of more than 25 state-of-the-art spectral reconstruction methods which are categorized as prior-based and data-driven methods. Simulations on open-source datasets show that prior-based methods are more suitable for rare data situations, while data-driven methods can unleash the full potential of deep learning in big data cases. We have identified current challenges faced by those methods (e.g., loss function, spectral accuracy, data generalization) and summarized a few trends for future work. With the rapid expansion in datasets and the advent of more advanced neural networks, learnable methods with fine feature representation abilities are very promising. This comprehensive review can serve as a fruitful reference source for peer researchers, thus paving the way for the development of computational hyperspectral imaging.
MeSH term(s)	Fruit ; Hyperspectral Imaging ; Neural Networks, Computer
Language	English
Publishing date	2022-07-13
Publishing country	England
Document type	Journal Article ; Review ; Research Support, Non-U.S. Gov't
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-022-16223-1
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Fabrication of large-scale scaffolds with microscale features using light sheet stereolithography.

Madrid-Sánchez, Alejandro / Duerr, Fabian / Nie, Yunfeng / Thienpont, Hugo / Ottevaere, Heidi

International journal of bioprinting

2022 Volume 9, Issue 2, Page(s) 650

Abstract: The common characteristics that make scaffolds suitable for human tissue substitutes include high porosity, microscale features, and pores interconnectivity. Too often, however, these characteristics are limiting factors for the scalability of different ... ...

Abstract	The common characteristics that make scaffolds suitable for human tissue substitutes include high porosity, microscale features, and pores interconnectivity. Too often, however, these characteristics are limiting factors for the scalability of different fabrication approaches, particularly in bioprinting techniques, in which either poor resolution, small areas, or slow processes hinder practical use in certain applications. An excellent example is bioengineered scaffolds for wound dressings, in which microscale pores in large surface-to-volume ratio scaffolds must be manufactured - ideally fast, precise, and cheap, and where conventional printing methods do not readily meet both ends. In this work, we propose an alternative vat photopolymerization technique to fabricate centimeter-scale scaffolds without losing resolution. We used laser beam shaping to first modify the profile of the voxels in 3D printing, resulting in a technology we refer to as light sheet stereolithography (LS-SLA). For proof of concept, we developed a system from commercially available off-the-shelf components to demonstrate strut thicknesses up to 12.8 ± 1.8 μm, tunable pore sizes ranging from 36 μm to 150 μm, and scaffold areas up to 21.4 mm × 20.6 mm printed in a short time. Furthermore, the potential to fabricate more complex and three-dimensional scaffolds was demonstrated with a structure composed of six layers, each rotated by 45° with respect to the previous. Besides the demonstrated high resolution and achievable large scaffold sizes, we found that LS-SLA has great potential for scaling-up of applied oriented technology for tissue engineering applications.
Language	English
Publishing date	2022-12-13
Publishing country	Singapore
Document type	Journal Article
ZDB-ID	2834694-4
ISSN	2424-8002 ; 2424-8002
ISSN (online)	2424-8002
ISSN	2424-8002
DOI	10.18063/ijb.v9i2.650
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Compact Shortwave Infrared Imaging Spectrometer Based on a Catadioptric Prism.

Feng, Lei / He, Xiaoying / Li, Yacan / Wei, Lidong / Nie, Yunfeng / Jing, Juanjuan / Zhou, Jinsong

Sensors (Basel, Switzerland)

2022 Volume 22, Issue 12

Abstract: This article demonstrates a compact prism imaging spectrometer method. A catadioptric curved prism is located at the secondary mirror position of the spectrometer and used to balance the aberrations, enlarge the dispersion width, and decrease the volume. ...

Abstract	This article demonstrates a compact prism imaging spectrometer method. A catadioptric curved prism is located at the secondary mirror position of the spectrometer and used to balance the aberrations, enlarge the dispersion width, and decrease the volume. A mathematical model of the prism and spectrometer is derived, which provides an optimal initial structure for a non-coaxial spectrometer, simplifying the optical design process and reducing the system volume. Using this method, a compact shortwave infrared imaging spectrometer with a 16° field of view is designed with an F-number/3, and the measured spectrum ranges from 0.95 to 2.5 μm. The performance is analyzed and evaluated. Laboratory testing results prove the excellent optical performance, and under the same specifications, the spectrometer length decreases by 40%.
Language	English
Publishing date	2022-06-18
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s22124611
Database	MEDical Literature Analysis and Retrieval System OnLINE

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