Aperture-scanning Fourier ptychography for 3D refocusing and super-resolution macroscopic imaging

We report an imaging scheme, termed aperture-scanning Fourier ptychography, for 3D refocusing and super-resolution macroscopic imaging. The reported scheme scans an aperture at the Fourier plane of an optical system and acquires the corresponding intensity images of the object. The acquired images are then synthesized in the frequency domain to recover a high-resolution complex sample wavefront; no phase information is needed in the recovery process. We demonstrate two applications of the reported scheme. In the first example, we use an aperture-scanning Fourier ptychography platform to recover the complex hologram of extended objects. The recovered hologram is then digitally propagated into different planes along the optical axis to examine the 3D structure of the object. We also demonstrate a reconstruction resolution better than the detector pixel limit (i.e., pixel super-resolution). In the second example, we develop a camera-scanning Fourier ptychography platform for super-resolution macroscopic imaging. By simply scanning the camera over different positions, we bypass the diffraction limit of the photographic lens and recover a super-resolution image of an object placed at the far field. This platform’s maximum achievable resolution is ultimately determined by the camera’s traveling range, not the aperture size of the lens. The FP scheme reported in this work may find applications in 3D object tracking, synthetic aperture imaging, remote sensing, and optical/electron/X-ray microscopy

Learned Multi-View Texture Super-Resolution

We present a super-resolution method capable of creating a high-resolution texture map for a virtual 3D object from a set of lower-resolution images of that object. Our architecture unifies the concepts of (i) multi-view super-resolution based on the redundancy of overlapping views and (ii) single-view super-resolution based on a learned prior of high-resolution (HR) image structure. The principle of multi-view super-resolution is to invert the image formation process and recover the latent HR texture from multiple lower-resolution projections. We map that inverse problem into a block of suitably designed neural network layers, and combine it with a standard encoder-decoder network for learned single-image super-resolution. Wiring the image formation model into the network avoids having to learn perspective mapping from textures to images, and elegantly handles a varying number of input views. Experiments demonstrate that the combination of multi-view observations and learned prior yields improved texture maps.Comment: 11 pages, 5 figures, 2019 International Conference on 3D Vision (3DV

동적 장면으로부터의 다중 물체 3차원 복원 기법 및 학습 기반의 깊이 초해상도 기법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2014. 2. 이경무.In this dissertation, a framework for reconstructing 3-dimensional shape of the multiple objects and the method for enhancing the resolution of 3-dimensional models, especially human face, are proposed. Conventional 3D reconstruction from multiple views is applicable to static scenes, in which the configuration of objects is fixed while the images are taken. In the proposed framework, the main goal is to reconstruct the 3D models of multiple objects in a more general setting where the configuration of the objects varies among views. This problem is solved by object-centered decomposition of the dynamic scenes using unsupervised co-recognition approach. Unlike conventional motion segmentation algorithms that require small motion assumption between consecutive views, co-recognition method provides reliable accurate correspondences of a same object among unordered and wide-baseline views. In order to segment each object region, the 3D sparse points obtained from the structure-from-motion are utilized. These points are relative reliable since both their geometric relation and photometric consistency are considered simultaneously to generate these 3D sparse points. The sparse points serve as automatic seed points for a seeded-segmentation algorithm, which makes the interactive segmentation work in non-interactive way. Experiments on various real challenging image sequences demonstrate the effectiveness of the proposed approach, especially in the presence of abrupt independent motions of objects. Obtaining high-density 3D model is also an important issue. Since the multi-view images used to reconstruct 3D model or the 3D imaging hardware such as the time-of-flight cameras or the laser scanners have their own natural upper limit of resolution, super-resolution method is required to increase the resolution of 3D data. This dissertation presents an algorithm to super-resolve the single human face model represented in 3D point cloud. The point cloud data is considered as an object-centered 3D data representation compared to the camera-centered depth images. While many researches are done for the super-resolution of intensity images and there exist some prior works on the depth image data, this is the first attempt to super-resolve the single set of 3D point cloud data without additional intensity or depth image observation of the object. This problem is solved by querying the previously learned database which contains corresponding high resolution 3D data associated with the low resolution data. The Markov Random Field(MRF) model is constructed on the 3D points, and the proper energy function is formulated as a multi-class labeling problem on the MRF. Experimental results show that the proposed method solves the super-resolution problem with high accuracy.Abstract i Contents ii List of Figures vii List of Tables xiii 1 Introduction 1 1.1 3D Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Dissertation Goal and Contribution . . . . . . . . . . . . . . . . . . . 2 1.3 Organization of Dissertation . . . . . . . . . . . . . . . . . . . . . . . 3 2 Background 7 2.1 Motion Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Image Super Resolution . . . . . . . . . . . . . . . . . . . . . . . . . 9 3 Multi-Object Reconstruction from Dynamic Scenes 13 3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 3.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.4 Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.4.1 Co-Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4.2 Integration of the Sub-Results . . . . . . . . . . . . . . . . . 25 3.5 Camera Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.6 Object Boundary Renement . . . . . . . . . . . . . . . . . . . . . . 28 3.7 3D Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.8 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.8.1 Qualitative Results . . . . . . . . . . . . . . . . . . . . . . . . 32 3.8.2 Quantitative Results . . . . . . . . . . . . . . . . . . . . . . . 39 3.8.3 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 3.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 4 Super Resolution for 3D Face Reconstruction 55 4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 4.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 4.3 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 4.4 Proposed Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.4.1 Local Patch . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.4.2 Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.4.3 Prior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 4.5 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.5.1 Training Data . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.5.2 Building Markov Network . . . . . . . . . . . . . . . . . . . . 75 4.5.3 Reconstructing Super-Resolved 3D Model . . . . . . . . . . . 76 4.6 Experiments and Results . . . . . . . . . . . . . . . . . . . . . . . . . 78 4.6.1 Quantitative Results . . . . . . . . . . . . . . . . . . . . . . . 78 4.6.2 Qualitative Results . . . . . . . . . . . . . . . . . . . . . . . . 81 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5 Conclusion 93 5.1 Summary of Dissertation . . . . . . . . . . . . . . . . . . . . . . . . . 93 5.2 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Bibliography 97 국문 초록 107Docto

High Resolution 3D Shape Texture from Multiple Videos

International audienceWe examine the problem of retrieving high resolution textures of objects observed in multiple videos under small object deformations. In the monocular case, the data redundancy necessary to reconstruct a high-resolution image stems from temporal accumulation. This has been vastly explored and is known as super-resolution. On the other hand, a handful of methods have considered the texture of a static 3D object observed from several cameras, where the data redundancy is obtained through the different viewpoints. We introduce a unified framework to leverage both possibilities for the estimation of a high resolution texture of an object. This framework uniformly deals with any related geometric variability introduced by the acquisition chain or by the evolution over time. To this goal we use 2D warps for all viewpoints and all temporal frames and a linear projection model from texture to image space. Despite its simplicity, the method is able to successfully handle different views over space and time. As shown experimentally, it demonstrates the interest of temporal information that improves the texture quality. Additionally, we also show that our method outperforms state of the art multi-view super-resolution methods that exist for the static case

Model based methods for locating, enhancing and recognising low resolution objects in video

Visual perception is our most important sense which enables us to detect and recognise objects even in low detail video scenes. While humans are able to perform such object detection and recognition tasks reliably, most computer vision algorithms struggle with wide angle surveillance videos that make automatic processing difficult due to low resolution and poor detail objects. Additional problems arise from varying pose and lighting conditions as well as non-cooperative subjects. All these constraints pose problems for automatic scene interpretation of surveillance video, including object detection, tracking and object recognition.Therefore, the aim of this thesis is to detect, enhance and recognise objects by incorporating a priori information and by using model based approaches. Motivated by the increasing demand for automatic methods for object detection, enhancement and recognition in video surveillance, different aspects of the video processing task are investigated with a focus on human faces. In particular, the challenge of fully automatic face pose and shape estimation by fitting a deformable 3D generic face model under varying pose and lighting conditions is tackled. Principal Component Analysis (PCA) is utilised to build an appearance model that is then used within a particle filter based approach to fit the 3D face mask to the image. This recovers face pose and person-specific shape information simultaneously. Experiments demonstrate the use in different resolution and under varying pose and lighting conditions. Following that, a combined tracking and super resolution approach enhances the quality of poor detail video objects. A 3D object mask is subdivided such that every mask triangle is smaller than a pixel when projected into the image and then used for model based tracking. The mask subdivision then allows for super resolution of the object by combining several video frames. This approach achieves better results than traditional super resolution methods without the use of interpolation or deblurring.Lastly, object recognition is performed in two different ways. The first recognition method is applied to characters and used for license plate recognition. A novel character model is proposed to create different appearances which are then matched with the image of unknown characters for recognition. This allows for simultaneous character segmentation and recognition and high recognition rates are achieved for low resolution characters down to only five pixels in size. While this approach is only feasible for objects with a limited number of different appearances, like characters, the second recognition method is applicable to any object, including human faces. Therefore, a generic 3D face model is automatically fitted to an image of a human face and recognition is performed on a mask level rather than image level. This approach does not require an initial pose estimation nor the selection of feature points, the face alignment is provided implicitly by the mask fitting process

Super-resolution photoacoustic and ultrasound imaging with sparse arrays

It has previously been demonstrated that model-based reconstruction methods relying on a priori knowledge of the imaging point spread function (PSF) coupled to sparsity priors on the object to image can provide super-resolution in photoacoustic (PA) or in ultrasound (US) imaging. Here, we experimentally show that such reconstruction also leads to super-resolution in both PA and US imaging with arrays having much less elements than used conventionally (sparse arrays). As a proof of concept, we obtained super-resolution PA and US cross-sectional images of microfluidic channels with only 8 elements of a 128-elements linear array using a reconstruction approach based on a linear propagation forward model and assuming sparsity of the imaged structure. Although the microchannels appear indistinguishable in the conventional delay-and-sum images obtained with all the 128 transducer elements, the applied sparsity-constrained model-based reconstruction provides super-resolution with down to only 8 elements. We also report simulation results showing that the minimal number of transducer elements required to obtain a correct reconstruction is fundamentally limited by the signal-to-noise ratio. The proposed method can be straigthforwardly applied to any transducer geometry, including 2D sparse arrays for 3D super-resolution PA and US imaging

Numerically Enhanced Stimulated Emission Depletion Microscopy with Adaptive Optics for Deep-Tissue Super-Resolved Imaging

Copyright © 2019 American Chemical Society. In stimulated emission depletion (STED) nanoscopy, the major origin of decreased signal-to-noise ratio within images can be attributed to sample photobleaching and strong optical aberrations. This is due to STED utilizing a high-power depletion laser (increasing the risk of photodamage), while the depletion beam is very sensitive to sample-induced aberrations. Here, we demonstrate a custom-built STED microscope with automated aberration correction that is capable of 3D super-resolution imaging through thick, highly aberrating tissue. We introduce and investigate a state of the art image denoising method by block-matching and collaborative 3D filtering (BM3D) to numerically enhance fine object details otherwise mixed with noise and further enhance the image quality. Numerical denoising provides an increase in the final effective resolution of the STED imaging of 31% using the well established Fourier ring correlation metric. Results achieved through the combination of aberration correction and tailored image processing are experimentally validated through super-resolved 3D imaging of axons in differentiated induced pluripotent stem cells growing under an 80 μm thick layer of tissue with lateral and axial resolution of 204 and 310 nm, respectively