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Special Issue on Computational Optical Imaging|27 Article(s)
Compressive Holographic Tomography of Color Diffuse Objects
Cheng Zhang, Zuo Yang, Xuelian Zhu, Min Pan, and Sui Wei
Using an incoherent scattering density function in the statistical sense to satisfy the hypothesis of sparse priori, the compressive holography of diffuse objects can realize the tomographic reconstruction of diffuse objects from multiple speckle patterns, avoiding speckle and crosstalk among defocusing images in different planes. In this paper, a single-wavelength illumination condition is extended to the red, green, and blue wavelengths. A new compressive holographic tomography method for color diffuse objects is proposed. A tomography model of diffuse objects under multi-wavelength illumination conditions is proposed, and the decompression reasoning method is used to effectively separate the three-dimensional incoherent density functions of different planes. The numerical simulation results show that the method can realize compressive reconstruction of the color tomography diffuse object from multiple two-dimensional color speckle patterns, and effectively suppress the speckle effect and crosstalk among defocusing images in different planes. Using an incoherent scattering density function in the statistical sense to satisfy the hypothesis of sparse priori, the compressive holography of diffuse objects can realize the tomographic reconstruction of diffuse objects from multiple speckle patterns, avoiding speckle and crosstalk among defocusing images in different planes. In this paper, a single-wavelength illumination condition is extended to the red, green, and blue wavelengths. A new compressive holographic tomography method for color diffuse objects is proposed. A tomography model of diffuse objects under multi-wavelength illumination conditions is proposed, and the decompression reasoning method is used to effectively separate the three-dimensional incoherent density functions of different planes. The numerical simulation results show that the method can realize compressive reconstruction of the color tomography diffuse object from multiple two-dimensional color speckle patterns, and effectively suppress the speckle effect and crosstalk among defocusing images in different planes.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111028 (2020)
Synthetic-Aperture Occlusion Removal Algorithm Using Microlens Array
Wentong Qian, hui Li, and Yuntao Wu
To address the overlapping and defocusing of images with blurred diffuse speckles, a synthetic-aperture occlusion removal algorithm using a microlens array (MLA) is proposed. In this study, a single light-field imaging system with an MLA is designed to acquire data of the original image. The synthetic aperture method is utilized to digitally focus for recognizing occlusion objects in a scene. According to the gray variance value, the threshold value is set to distinguish an occlusion object from the target object. Then, the occlusion object is removed when it is recognized, and the target object can be extracted by refocusing. The experiment shows that the no-reference image quality assessment value is improved by at least 18.83% compared with that using other similar algorithms. The proposed method not only improves the image quality, but also presents 3D information of the scene. It also obtains the target image with a high contrast and high signal-to-noise ratio. To address the overlapping and defocusing of images with blurred diffuse speckles, a synthetic-aperture occlusion removal algorithm using a microlens array (MLA) is proposed. In this study, a single light-field imaging system with an MLA is designed to acquire data of the original image. The synthetic aperture method is utilized to digitally focus for recognizing occlusion objects in a scene. According to the gray variance value, the threshold value is set to distinguish an occlusion object from the target object. Then, the occlusion object is removed when it is recognized, and the target object can be extracted by refocusing. The experiment shows that the no-reference image quality assessment value is improved by at least 18.83% compared with that using other similar algorithms. The proposed method not only improves the image quality, but also presents 3D information of the scene. It also obtains the target image with a high contrast and high signal-to-noise ratio.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111027 (2020)
Hyperspectral Imaging System Using Electronic Multi-Slot Combination Coding
Shijie Liu, Chunlai Li, Rui Xu, Guoliang Tang, and Jianyu Wang
A coded aperture spectral imaging system uses a spatial light modulator to encode target information. It subsequently maps the signal onto a two-dimensional detector array and forms spatial and spectral aliasing information. Further, a spectral data cube can be reconstructed using a suitable reconstruction algorithm. In this study, we propose a multi-slot combination coding method, which is encoded in only one direction, to improve the coding efficiency. This procedure is selected because dispersion occurs in only one direction. When compared with the two-dimensional random coding method that is currently being applied, this approach simplifies the mathematical model and its analysis while reducing the coding complexity,under the premise of obtaining identical reconstruction results. Further, the switching characteristics of the liquid crystal light valve are used in the coding. The spectral imaging system is assembled by incorporating a PGP (prism-transmission grating-prism) beam-splitting component. Different sampling rate experiments are conducted, and highly accurate recovery results are obtained. The feasibility of the proposed coding method can result in innovation in the future coded spectral imaging systems. A coded aperture spectral imaging system uses a spatial light modulator to encode target information. It subsequently maps the signal onto a two-dimensional detector array and forms spatial and spectral aliasing information. Further, a spectral data cube can be reconstructed using a suitable reconstruction algorithm. In this study, we propose a multi-slot combination coding method, which is encoded in only one direction, to improve the coding efficiency. This procedure is selected because dispersion occurs in only one direction. When compared with the two-dimensional random coding method that is currently being applied, this approach simplifies the mathematical model and its analysis while reducing the coding complexity,under the premise of obtaining identical reconstruction results. Further, the switching characteristics of the liquid crystal light valve are used in the coding. The spectral imaging system is assembled by incorporating a PGP (prism-transmission grating-prism) beam-splitting component. Different sampling rate experiments are conducted, and highly accurate recovery results are obtained. The feasibility of the proposed coding method can result in innovation in the future coded spectral imaging systems.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111026 (2020)
Reconstruction of Variable Exponential Regularization for Wide-Field Polarization-Modulated Imaging
Qiong Wu, Kun Gan, Zhenzhou Zhang, Zeyang Dou, Weiping Liu, and Jichuan Xiong
In this study, a regularization method of point spread function (PSF) estimation and image reconstruction considering multiple blur factors about image degradation is proposed for wide-field polarization-modulated microscopic imaging. The adaptive regularization model of variable exponential function is used for the PSF estimation, which is degraded due to the fitting deviation of polarization-angle modulation curve, the degradation of optical system, and discrete under-sampling of charge-coupled device (CCD) during the blurring process. The aim is to fully utilize the acquired image content features, adaptively select a variable regularization norm, and effectively restrain the staircase effect of total variation regularization and the disadvantage of poor edge-preserving property of Tikhonov regularization. The optimized Split-Bregman iterative algorithm is adopted in the solution process, which can ensure the estimation accuracy and reduce the computational complexity as well. Experimental results show that the proposed method can effectively estimate the degraded PSF and improve the noise robustness of image reconstruction. In this study, a regularization method of point spread function (PSF) estimation and image reconstruction considering multiple blur factors about image degradation is proposed for wide-field polarization-modulated microscopic imaging. The adaptive regularization model of variable exponential function is used for the PSF estimation, which is degraded due to the fitting deviation of polarization-angle modulation curve, the degradation of optical system, and discrete under-sampling of charge-coupled device (CCD) during the blurring process. The aim is to fully utilize the acquired image content features, adaptively select a variable regularization norm, and effectively restrain the staircase effect of total variation regularization and the disadvantage of poor edge-preserving property of Tikhonov regularization. The optimized Split-Bregman iterative algorithm is adopted in the solution process, which can ensure the estimation accuracy and reduce the computational complexity as well. Experimental results show that the proposed method can effectively estimate the degraded PSF and improve the noise robustness of image reconstruction.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111025 (2020)
Improved Correction Algorithm for Harmonic- and Intensity-Related Errors in Time-of-Flight Cameras
Bin Jiang, and Xiangliang Jin
This study uses a B-spline surface error fitting function based on reflected light intensity and depth two-dimensional variable with additive weights to correct the weak light intensity-related depth error that occurs because of the low reflectivity or long distance of the target scene. In contrast to the traditional B-spline surface error fitting, the improved model uses weight parameters to optimize partial control points of each surface to obtain a more accurate error-fitting surface. It allows average correction errors to be less than 2 mm, which is nearly three times the accuracy of the traditional model. Furthermore, the additive weight parameter is added to each control point, which ensures that the parameter matrices of control points and additive weight can be obtained in a single step during the camera parameter calibration process,overcoming the issue that the multiplicative weight matrix cannot be obtained by the least squares method. Moreover, considering that the harmonic-related error is also connected to depth, the correction of the harmonic-related error is unified into the proposed correction method. This study uses a B-spline surface error fitting function based on reflected light intensity and depth two-dimensional variable with additive weights to correct the weak light intensity-related depth error that occurs because of the low reflectivity or long distance of the target scene. In contrast to the traditional B-spline surface error fitting, the improved model uses weight parameters to optimize partial control points of each surface to obtain a more accurate error-fitting surface. It allows average correction errors to be less than 2 mm, which is nearly three times the accuracy of the traditional model. Furthermore, the additive weight parameter is added to each control point, which ensures that the parameter matrices of control points and additive weight can be obtained in a single step during the camera parameter calibration process,overcoming the issue that the multiplicative weight matrix cannot be obtained by the least squares method. Moreover, considering that the harmonic-related error is also connected to depth, the correction of the harmonic-related error is unified into the proposed correction method.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111024 (2020)
Coplanarity Inspection Method for Integrated Circuit Pins Based on Single Image
Fupei Wu, Shukai Zhu, and Shengping Li
Inspection of the coplanarity of pins in an integrated circuit (IC) is a very important process for ensuring the mounting quality of IC. In this paper, a coplanarity inspection method for IC pins based on a single image is proposed. First, a relation model that contains a camera, light source, and measured surface is constructed. This model is based on a monocular vision system. Then, the light intensity calibration method of a light emitting diode (LED) ring-structured light source is described, and the correlative parameter of the measured IC pin material is experimentally obtained. Further, a method for estimating the height information based on the constructed relationship model of light intensity and image grayscale is presented. Finally, the surface three-dimensional morphology of the IC pins and their solder joints are recovered using the height information. In comparison with the actual measurement results for IC pins and their solder joints, the experimental measurement results obtained in this work show that the measurement error for height is less than ±0.08 mm, and the relative error is within -2.6%, which verifies the effectiveness of the proposed method. Inspection of the coplanarity of pins in an integrated circuit (IC) is a very important process for ensuring the mounting quality of IC. In this paper, a coplanarity inspection method for IC pins based on a single image is proposed. First, a relation model that contains a camera, light source, and measured surface is constructed. This model is based on a monocular vision system. Then, the light intensity calibration method of a light emitting diode (LED) ring-structured light source is described, and the correlative parameter of the measured IC pin material is experimentally obtained. Further, a method for estimating the height information based on the constructed relationship model of light intensity and image grayscale is presented. Finally, the surface three-dimensional morphology of the IC pins and their solder joints are recovered using the height information. In comparison with the actual measurement results for IC pins and their solder joints, the experimental measurement results obtained in this work show that the measurement error for height is less than ±0.08 mm, and the relative error is within -2.6%, which verifies the effectiveness of the proposed method.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111023 (2020)
Single-Photon Compressive Imaging Based on Residual Codec Network
Yanqiu Guan, Qiurong Yan, Shengtao Yang, Bing Li, Qianqian Cao, and Zheyu Fang
When performing high-resolution imaging using a single-photon compressive technique, a long imaging time is required owing to numerous measurements and a large number of image-reconstruction calculations. We demonstrate a sampling-and-reconstruction-integrated residual codec network, namely SRIED-Net, for single-photon compressive imaging. We use the binarized fully connected layer as the first layer of the network and train it into a binary-measurement matrix to directly load onto the digital micromirror device for efficient compressive sampling. The remaining layers of the network are used to quickly reconstruct the compressed sensing image. We compare the effects of the compressive sampling rate, measurement matrix, and reconstruction algorithm on imaging performance through a series of simulations and system experiments. The experimental results show that SRIED-Net is superior to the current advanced iterative algorithm TVAL3 at a low measurement rate and that its imaging quality is similar to that of TVAL3 at a high measurement rate. It is superior to current deep-learning-based methods at all measurement rates. When performing high-resolution imaging using a single-photon compressive technique, a long imaging time is required owing to numerous measurements and a large number of image-reconstruction calculations. We demonstrate a sampling-and-reconstruction-integrated residual codec network, namely SRIED-Net, for single-photon compressive imaging. We use the binarized fully connected layer as the first layer of the network and train it into a binary-measurement matrix to directly load onto the digital micromirror device for efficient compressive sampling. The remaining layers of the network are used to quickly reconstruct the compressed sensing image. We compare the effects of the compressive sampling rate, measurement matrix, and reconstruction algorithm on imaging performance through a series of simulations and system experiments. The experimental results show that SRIED-Net is superior to the current advanced iterative algorithm TVAL3 at a low measurement rate and that its imaging quality is similar to that of TVAL3 at a high measurement rate. It is superior to current deep-learning-based methods at all measurement rates.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111022 (2020)
Single-Image Refocusing Using Light Field Synthesis and Circle of Confusion Rendering
Qi Wang, and Yutian Fu
A method to dynamically refocus a single image is presented; by combining deep learning-based light field synthesis with geometric structure-based circle of confusion rendering, it simulates the light field refocusing effect. In the proposed method, the depth map is estimated and converted into disparity, and then the circle of confusion diameter is measured at different depths to resample the pixels. Two neural network structures are designed, supervised by multi-views and refocused images of the light field camera. Experiments are conducted on multiple datasets and real scenes. Compared with other techniques, the results obtained using the proposed method show superior visual performance and evaluation indicators, along with an acceptable computational cost, with the peak signal-to-noise ratio and structural similarity index reaching 34.55 and 0.937, respectively. A method to dynamically refocus a single image is presented; by combining deep learning-based light field synthesis with geometric structure-based circle of confusion rendering, it simulates the light field refocusing effect. In the proposed method, the depth map is estimated and converted into disparity, and then the circle of confusion diameter is measured at different depths to resample the pixels. Two neural network structures are designed, supervised by multi-views and refocused images of the light field camera. Experiments are conducted on multiple datasets and real scenes. Compared with other techniques, the results obtained using the proposed method show superior visual performance and evaluation indicators, along with an acceptable computational cost, with the peak signal-to-noise ratio and structural similarity index reaching 34.55 and 0.937, respectively.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111021 (2020)
Object Detection of Remote Sensing Image Based on Improved Rotation Region Proposal Network
Yuan Dai, Benshun Yi, Jinsheng Xiao, Junfeng Lei, Le Tong, and Zhiqin Cheng
In this study, the integration of the rotation region proposal network with Faster R-CNN network along with an improved remote sensing image object detection method based on the convolutional neural network is proposed. The aim is two-fold: 1) to realize rapid and precise detection of remote sensing image objects; 2) to address the problem caused by objects with rotated angle. Compared to the mainstream target detection methods, the proposed method introduces the rotation factor to the region proposal network and generates proposal regions with different directions, aiming at the characteristics of variable direction and relative aggregation of most targets in the remote sensing image. The addition of a convolution layer before the fully connected layer of the Faster R-CNN network has the advantages of reducing the feature parameters, enhancing the performance of classifiers, and avoiding over-fitting. Compared with the state-of-the-art object detection methods, the proposed algorithm is able to combine the features extracted by the convolutional neural network in the rotation region proposal network with the multi-scale features. Therefore, significant improvement in remote sensing image object detection can be achieved. In this study, the integration of the rotation region proposal network with Faster R-CNN network along with an improved remote sensing image object detection method based on the convolutional neural network is proposed. The aim is two-fold: 1) to realize rapid and precise detection of remote sensing image objects; 2) to address the problem caused by objects with rotated angle. Compared to the mainstream target detection methods, the proposed method introduces the rotation factor to the region proposal network and generates proposal regions with different directions, aiming at the characteristics of variable direction and relative aggregation of most targets in the remote sensing image. The addition of a convolution layer before the fully connected layer of the Faster R-CNN network has the advantages of reducing the feature parameters, enhancing the performance of classifiers, and avoiding over-fitting. Compared with the state-of-the-art object detection methods, the proposed algorithm is able to combine the features extracted by the convolutional neural network in the rotation region proposal network with the multi-scale features. Therefore, significant improvement in remote sensing image object detection can be achieved.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111020 (2020)
Method for Interpolation of Missing Point Cloud Based on Phase Mapping in Binocular Vision
Chenghang Li, Junpeng Xue, Wei Lang, and Qican Zhang
In this study, we propose a method to interpolate missing data that can be attributed to a disparity hole in binocular vision phase matching based on the reliable phase data obtained using a single camera. In the calibration stage, the measurement system only needs to consider the plane phase and height data as reference. Further, the implicit phase-height mapping relation can be established based on the phase and height difference observed with respect to the effective three-dimensional point clouds around the area of the hole and the reference plane, and the missing point clouds are reconstructed and interpolated using the reliable phase data obtained using a single camera. The interpolation of the missing point cloud data of a standard sample denotes a reconstruction accuracy of 0.07 mm. Furthermore, binocular measurement and hole interpolation are conducted using a facial mask and gourd model, and the results denote that the proposed method can appropriately interpolate the missing point cloud data in the occluded areas. In this study, we propose a method to interpolate missing data that can be attributed to a disparity hole in binocular vision phase matching based on the reliable phase data obtained using a single camera. In the calibration stage, the measurement system only needs to consider the plane phase and height data as reference. Further, the implicit phase-height mapping relation can be established based on the phase and height difference observed with respect to the effective three-dimensional point clouds around the area of the hole and the reference plane, and the missing point clouds are reconstructed and interpolated using the reliable phase data obtained using a single camera. The interpolation of the missing point cloud data of a standard sample denotes a reconstruction accuracy of 0.07 mm. Furthermore, binocular measurement and hole interpolation are conducted using a facial mask and gourd model, and the results denote that the proposed method can appropriately interpolate the missing point cloud data in the occluded areas.
Acta Optica Sinica
- Publication Date: Jan. 02, 2020
- Vol. 40, Issue 1, 0111019 (2020)
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