Fourier ptychographic microscopy(FPM)is an innovative computational microscopy approach that enables high-throughput imaging with high resolution,wide field of view,and quantitative phase imaging(QPI)by simultaneously...Fourier ptychographic microscopy(FPM)is an innovative computational microscopy approach that enables high-throughput imaging with high resolution,wide field of view,and quantitative phase imaging(QPI)by simultaneously capturing bright-field and dark-field images.However,effectively utilizing dark-field intensity images,including both normally exposed and overexposed data,which contain valuable high-angle illumination information,remains a complex challenge.Successfully extracting and applying this information could significantly enhance phase reconstruction,benefiting processes such as virtual staining and QPI imaging.To address this,we introduce a multi-exposure image fusion(MEIF)framework that optimizes dark-field information by incorporating it into the FPM preprocessing workflow.MEIF increases the data available for reconstruction without requiring changes to the optical setup.We evaluate the framework using both feature-domain and traditional FPM,demonstrating that it achieves substantial improvements in intensity resolution and phase information for biological samples that exceed the performance of conventional high dynamic range(HDR)methods.This image preprocessing-based information-maximization strategy fully leverages existing datasets and offers promising potential to drive advancements in fields such as microscopy,remote sensing,and crystallography.展开更多
The counter-meshing gears （CMG） discriminator is a mechanically coded lock, which is used to prevent the occurrence of High Consequence Events. This paper advanced a new kind of self-assembly metal CMG discriminator...The counter-meshing gears （CMG） discriminator is a mechanically coded lock, which is used to prevent the occurrence of High Consequence Events. This paper advanced a new kind of self-assembly metal CMG discriminator based on multi-exposure LiGA like process and sacrificial layer process. The new CMG discriminator has the following characters except low cost： 1） it has only discrimination teeth sections; 2） the thickness of each gear layer exceeds one hundred micrometers; 3） it is axially driven by a separate dectronic magnetic micromotor directly; 4） its CMG is made of metal and is batch fabricated in the assembled state; 5） it is prevented from rotating in the opposite direction by pawl/ratchet wheel mechanism; 6） it has simpler structure. This device has better strength and reliability in abnormal environment compared to the existing surface micro machining （SMM） discriminator.展开更多
Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high qualit...Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies scenes.展开更多
In motion estimation, illumination change is always a troublesome obstacle, which often causes severely per- formance reduction of optical flow computation. The essential reason is that most of estimation methods fail...In motion estimation, illumination change is always a troublesome obstacle, which often causes severely per- formance reduction of optical flow computation. The essential reason is that most of estimation methods fail to formalize a unified definition in color or gradient domain for diverse environmental changes. In this paper, we propose a new solution based on deep convolutional networks to solve the key issue. Our idea is to train deep convolutional networks to represent the complex motion features under illumination change, and further predict the final optical flow fields. To this end, we construct a training dataset of multi-exposure image pairs by performing a series of non-linear adjustments in the traditional datasets of optical.flow estimation. Our multi-exposure flow networks （MEFNet） model consists of three main components： low-level feature network, fusion feature network, and motion estimation network. The former two components belong to the contracting part of our model in order to extract and represent the multi-exposure motion features; the third component is the expanding part of our model in order to learn and predict the high-quality optical flow. Compared with many state- of-the-art methods, our motion estimation method can eliminate the obstacle of illumination change and yield optical flow results with competitive accuracy and time efficiency. Moreover, the good performance of our model is also demonstrated in some multi-exposure video applications, like HDR （high dynamic range） composition and flicker removal.展开更多
Vehicle detection in dim light has always been a challenging task.In addition to the unavoidable noise,the uneven spatial distribution of light and dark due to vehicle lights and street lamps can further make the prob...Vehicle detection in dim light has always been a challenging task.In addition to the unavoidable noise,the uneven spatial distribution of light and dark due to vehicle lights and street lamps can further make the problem more difficult.Conventional image enhancement methods may produce over smoothing or over exposure problems,causing irreversible information loss to the vehicle targets to be subsequently detected.Therefore,we propose a multi-exposure generation and fusion network.In the multi-exposure generation network,we employ a single gated convolutional recurrent network with two-stream progressive exposure input to generate intermediate images with gradually increasing exposure,which are provided to the multi-exposure fusion network after a spatial attention mechanism.Then,a pre-trained vehicle detection model in normal light is used as the basis of the fusion network,and the two models are connected using the convolutional kernel channel dimension expansion technique.This allows the fusion module to provide vehicle detection information,which can be used to guide the generation network tofine-tune the parameters and thus complete end-to-end enhancement and training.By coupling the two parts,we can achieve detail interaction and feature fusion under different lighting conditions.Our experimental results demonstrate that our proposed method is better than the state-of-the-art detection methods after image luminance enhancement on the ODDS dataset.展开更多
Mueller matrix polarimetry(MMP)has been proven to be a powerful tool for characterizing the microstructural features of biological samples in biomedical research and clinical diagnostics.However,the traditional Muelle...Mueller matrix polarimetry(MMP)has been proven to be a powerful tool for characterizing the microstructural features of biological samples in biomedical research and clinical diagnostics.However,the traditional Mueller matrix(MM)imaging technique based on single exposure has a limited dynamic range,leading to poor polarization image quality for biological samples with signi-cant contrast variations.In this study,we propose a novel method to generate high dynamic range(HDR)MM images based on a multi-exposure fusion algorithm.By employing an optimal exposure selection strategy for transmission imaging and a multi-exposure weighted averaging strategy for backscattering imaging,the method expands the dynamic range while accurately preserving the polarization information of the samples.Experiments of sliced and bulk tissues demonstrate that the proposed method signi¯cantly suppresses the scattering noise and improves the quality of extracted polarization parameter images,especially in accurate distinction of di®erent pathological areas.These results highlight the potential of HDR MM imaging technology in extracting polarization information from complex biological samples with high resolution and contrast.展开更多
In fringe projection profilometry 3D measurement systems,the measurement of surfaces with high variability in reflectivity poses a challenge due to the limited dynamic range of cameras.The main solution involves using...In fringe projection profilometry 3D measurement systems,the measurement of surfaces with high variability in reflectivity poses a challenge due to the limited dynamic range of cameras.The main solution involves using multiple exposures to modulate fringe intensity;however,it is inefficient.In this study,we introduce an attention-guided end-to-end phase calculation network to accelerate the multi-exposure structured light process for high dynamic range(HDR)measurements.We use attention modules to guide feature selection,enhancing relevant features and suppressing irrelevant features.Using the 12-step phase-shifting profilometry(PSP)as ground truth,our method accurately extracts the sine and cosine components of the fundamental frequency from a single pattern to retrieve the absolute phases.Tested on our metallic dataset requiring HDR imaging,our method achieves an absolute phase error of 0.084,close to that of the six-step PSP method(0.069),while using only 16.7%of the time.On the ceramic dataset,our method achieves 0.021 phase error,close to that of the four-step PSP(0.012).In quantitative measurements,our method achieves an accuracy of approximately 40μm on standard spheres and plates.Overall,our method preserves the accuracy of multi-exposure PSP methods while significantly accelerating the 3D reconstruction process.展开更多
Functional periodic structures have attracted significant interest due to their natural capabilities in regulating surface energy, surface effective refractive index, and diffraction. Several technologies are used for...Functional periodic structures have attracted significant interest due to their natural capabilities in regulating surface energy, surface effective refractive index, and diffraction. Several technologies are used for the fabrication of these functional structures. The laser interference technique in particular has received attention because of its simplicity, low cost, and high-efficiency fabrication of large-area, micro/nanometer-scale, and periodically patterned structures in air conditions. Here, we reviewed the work on laser interference fabrication of large-area functional periodic structures for antireflection, self-cleaning, and superhydrophobicity based on our past and current research. For the common cases, four-beam interference and multi-exposure of two-beam interference were emphasized for their setup, structure diversity, and various applications for antireflection, self-cleaning, and superhydrophobicity. The relations between multi-beam interference and multi-exposure of two-beam interference were compared theoretically and experimentally. Nanostructures as a template for growing nanocrystals were also shown to present future possible applications in surface chemical control. Perspectives on future directions and applications for laser interference were presented.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.12104500)the Key Research and Development Projects of Shaanxi Province of China(Grant No.2023-YBSF-263).
文摘Fourier ptychographic microscopy(FPM)is an innovative computational microscopy approach that enables high-throughput imaging with high resolution,wide field of view,and quantitative phase imaging(QPI)by simultaneously capturing bright-field and dark-field images.However,effectively utilizing dark-field intensity images,including both normally exposed and overexposed data,which contain valuable high-angle illumination information,remains a complex challenge.Successfully extracting and applying this information could significantly enhance phase reconstruction,benefiting processes such as virtual staining and QPI imaging.To address this,we introduce a multi-exposure image fusion(MEIF)framework that optimizes dark-field information by incorporating it into the FPM preprocessing workflow.MEIF increases the data available for reconstruction without requiring changes to the optical setup.We evaluate the framework using both feature-domain and traditional FPM,demonstrating that it achieves substantial improvements in intensity resolution and phase information for biological samples that exceed the performance of conventional high dynamic range(HDR)methods.This image preprocessing-based information-maximization strategy fully leverages existing datasets and offers promising potential to drive advancements in fields such as microscopy,remote sensing,and crystallography.
文摘The counter-meshing gears （CMG） discriminator is a mechanically coded lock, which is used to prevent the occurrence of High Consequence Events. This paper advanced a new kind of self-assembly metal CMG discriminator based on multi-exposure LiGA like process and sacrificial layer process. The new CMG discriminator has the following characters except low cost： 1） it has only discrimination teeth sections; 2） the thickness of each gear layer exceeds one hundred micrometers; 3） it is axially driven by a separate dectronic magnetic micromotor directly; 4） its CMG is made of metal and is batch fabricated in the assembled state; 5） it is prevented from rotating in the opposite direction by pawl/ratchet wheel mechanism; 6） it has simpler structure. This device has better strength and reliability in abnormal environment compared to the existing surface micro machining （SMM） discriminator.
基金Supported by the National Natural Science Foundation of China(No.61308099,61304032)
文摘Due to the existing limited dynamic range a camera cannot reveal all the details in a high-dynamic range scene. In order to solve this problem,this paper presents a multi-exposure fusion method for getting high quality images in high dynamic range scene. First,a set of multi-exposure images is obtained by multiple exposures in a same scene and their brightness condition is analyzed. Then,multi-exposure images under the same scene are decomposed using dual-tree complex wavelet transform( DT-CWT),and their low and high frequency components are obtained. Weight maps according to the brightness condition are assigned to the low components for fusion. Maximizing the region Sum Modified-Laplacian( SML) is adopted for high-frequency components fusing. Finally,the fused image is acquired by subjecting the low and high frequency coefficients to inverse DT-CWT.Experimental results show that the proposed approach generates high quality results with uniform distributed brightness and rich details. The proposed method is efficient and robust in varies scenes.
基金This work was supported by the National Natural Science Foundation of China under Grant Nos. 61303093, 61472245, and 61402278, the Innovation Program of the Science and Technology Commission of Shanghai Municipality of China under Grant No. 16511101300, and the Gaofeng Film Discipline Grant of Shanghai Municipal Education Commission of China.
文摘In motion estimation, illumination change is always a troublesome obstacle, which often causes severely per- formance reduction of optical flow computation. The essential reason is that most of estimation methods fail to formalize a unified definition in color or gradient domain for diverse environmental changes. In this paper, we propose a new solution based on deep convolutional networks to solve the key issue. Our idea is to train deep convolutional networks to represent the complex motion features under illumination change, and further predict the final optical flow fields. To this end, we construct a training dataset of multi-exposure image pairs by performing a series of non-linear adjustments in the traditional datasets of optical.flow estimation. Our multi-exposure flow networks （MEFNet） model consists of three main components： low-level feature network, fusion feature network, and motion estimation network. The former two components belong to the contracting part of our model in order to extract and represent the multi-exposure motion features; the third component is the expanding part of our model in order to learn and predict the high-quality optical flow. Compared with many state- of-the-art methods, our motion estimation method can eliminate the obstacle of illumination change and yield optical flow results with competitive accuracy and time efficiency. Moreover, the good performance of our model is also demonstrated in some multi-exposure video applications, like HDR （high dynamic range） composition and flicker removal.
基金supported in part by the Science and Technology Innovation foundation(No.JSGG20210802152811033).
文摘Vehicle detection in dim light has always been a challenging task.In addition to the unavoidable noise,the uneven spatial distribution of light and dark due to vehicle lights and street lamps can further make the problem more difficult.Conventional image enhancement methods may produce over smoothing or over exposure problems,causing irreversible information loss to the vehicle targets to be subsequently detected.Therefore,we propose a multi-exposure generation and fusion network.In the multi-exposure generation network,we employ a single gated convolutional recurrent network with two-stream progressive exposure input to generate intermediate images with gradually increasing exposure,which are provided to the multi-exposure fusion network after a spatial attention mechanism.Then,a pre-trained vehicle detection model in normal light is used as the basis of the fusion network,and the two models are connected using the convolutional kernel channel dimension expansion technique.This allows the fusion module to provide vehicle detection information,which can be used to guide the generation network tofine-tune the parameters and thus complete end-to-end enhancement and training.By coupling the two parts,we can achieve detail interaction and feature fusion under different lighting conditions.Our experimental results demonstrate that our proposed method is better than the state-of-the-art detection methods after image luminance enhancement on the ODDS dataset.
基金supported by the Cross-research Innovation Fund of the International Graduate School at Shenzhen,Tsinghua University(JC2021002).
文摘Mueller matrix polarimetry(MMP)has been proven to be a powerful tool for characterizing the microstructural features of biological samples in biomedical research and clinical diagnostics.However,the traditional Mueller matrix(MM)imaging technique based on single exposure has a limited dynamic range,leading to poor polarization image quality for biological samples with signi-cant contrast variations.In this study,we propose a novel method to generate high dynamic range(HDR)MM images based on a multi-exposure fusion algorithm.By employing an optimal exposure selection strategy for transmission imaging and a multi-exposure weighted averaging strategy for backscattering imaging,the method expands the dynamic range while accurately preserving the polarization information of the samples.Experiments of sliced and bulk tissues demonstrate that the proposed method signi¯cantly suppresses the scattering noise and improves the quality of extracted polarization parameter images,especially in accurate distinction of di®erent pathological areas.These results highlight the potential of HDR MM imaging technology in extracting polarization information from complex biological samples with high resolution and contrast.
基金funded by Shenzhen Science and Technology Program(Grant JCYJ20240813112003005)by The Major Key Project of Pengcheng Laboratory(PCL2023A09).
文摘In fringe projection profilometry 3D measurement systems,the measurement of surfaces with high variability in reflectivity poses a challenge due to the limited dynamic range of cameras.The main solution involves using multiple exposures to modulate fringe intensity;however,it is inefficient.In this study,we introduce an attention-guided end-to-end phase calculation network to accelerate the multi-exposure structured light process for high dynamic range(HDR)measurements.We use attention modules to guide feature selection,enhancing relevant features and suppressing irrelevant features.Using the 12-step phase-shifting profilometry(PSP)as ground truth,our method accurately extracts the sine and cosine components of the fundamental frequency from a single pattern to retrieve the absolute phases.Tested on our metallic dataset requiring HDR imaging,our method achieves an absolute phase error of 0.084,close to that of the six-step PSP method(0.069),while using only 16.7%of the time.On the ceramic dataset,our method achieves 0.021 phase error,close to that of the four-step PSP(0.012).In quantitative measurements,our method achieves an accuracy of approximately 40μm on standard spheres and plates.Overall,our method preserves the accuracy of multi-exposure PSP methods while significantly accelerating the 3D reconstruction process.
基金Acknowledgements H. B. Sun thanks the National Key Research and Development Program of China and the National Natural Science Foundation of China （Grant Nos. 2017YFBI104300, 61590930, 20150203008GX, and 61605055）.
文摘Functional periodic structures have attracted significant interest due to their natural capabilities in regulating surface energy, surface effective refractive index, and diffraction. Several technologies are used for the fabrication of these functional structures. The laser interference technique in particular has received attention because of its simplicity, low cost, and high-efficiency fabrication of large-area, micro/nanometer-scale, and periodically patterned structures in air conditions. Here, we reviewed the work on laser interference fabrication of large-area functional periodic structures for antireflection, self-cleaning, and superhydrophobicity based on our past and current research. For the common cases, four-beam interference and multi-exposure of two-beam interference were emphasized for their setup, structure diversity, and various applications for antireflection, self-cleaning, and superhydrophobicity. The relations between multi-beam interference and multi-exposure of two-beam interference were compared theoretically and experimentally. Nanostructures as a template for growing nanocrystals were also shown to present future possible applications in surface chemical control. Perspectives on future directions and applications for laser interference were presented.
