722 research outputs found

    Quantitative analysis of microscopy

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    Particle tracking is an essential tool for the study of dynamics of biological processes. The dynamics of these processes happens in three-dimensional (3D) space as the biological structures themselves are 3D. The focus of this thesis is on the development of single particle tracking methods for analysis of the dynamics of biological processes through the use of image processing techniques. Firstly, introduced is a novel particle tracking method that works with two-dimensional (2D) image data. This method uses the theory of Haar-like features for particle detection and trajectory linking is achieved using a combination of three Kalman filters within an interacting multiple models framework. The trajectory linking process utilises an extended state space variable which better describe the morphology and intensity profiles of the particles under investigation at their current position. This tracking method is validated using both 2D synthetically generated images as well as 2D experimentally collected images. It is shown that this method outperforms 14 other stateof-the-art methods. Next this method is used to analyse the dynamics of fluorescently labelled particles using a live-cell fluorescence microscopy technique, specifically a variant of the super-resolution (SR) method PALM, spt-PALM. From this application, conclusions about the organisation of the proteins under investigation at the cell membrane are drawn. Introduced next is a second particle tracking method which is highly efficient and capable of working with both 2D and 3D image data. This method uses a novel Haar-inspired feature for particle detection, drawing inspiration from the type of particles to be detected which are typically circular in 2D space and spherical in 3D image space. Trajectory linking in this method utilises a global nearest neighbour methodology incorporating both motion models to describe the motion of the particles under investigation and a further extended state space variable describing many more aspects of the particles to be linked. This method is validated using a variety of both 2D and 3D synthetic image data. The methods performance is compared with 14 other state-of-the-art methods showing it to be one of the best overall performing methods. Finally, analysis tools to study a SR image restoration method developed by our research group, referred to as Translation Microscopy (TRAM) are investigated [1]. TRAM can be implemented on any standardised microscope and deliver an improvement in resolution of up to 7-fold. However, the results from TRAM and other SR imaging methods require specialised tools to validate and analyse them. Tools have been developed to validate that TRAM performs correctly using a specially designed ground truth. Furthermore, through analysis of results on a biological sample corroborate other published results based on the size of biological structures, showing again that TRAM performs as expected.EPSC

    Deep learning for intracellular particle tracking and motion analysis

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    Online Super-Resolution For Fibre-Bundle-Based Confocal Laser Endomicroscopy

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    Probe-based Confocal Laser Endomicroscopy (pCLE) produces microscopic images enabling real-time in vivo optical biopsy. However, the miniaturisation of the optical hardware, specifically the reliance on an optical fibre bundle as an imaging guide, fundamentally limits image quality by producing artefacts, noise, and relatively low contrast and resolution. The reconstruction approaches in clinical pCLE products do not fully alleviate these problems. Consequently, image quality remains a barrier that curbs the full potential of pCLE. Enhancing the image quality of pCLE in real-time remains a challenge. The research in this thesis is a response to this need. I have developed dedicated online super-resolution methods that account for the physics of the image acquisition process. These methods have the potential to replace existing reconstruction algorithms without interfering with the fibre design or the hardware of the device. In this thesis, novel processing pipelines are proposed for enhancing the image quality of pCLE. First, I explored a learning-based super-resolution method that relies on mapping from the low to the high-resolution space. Due to the lack of high-resolution pCLE, I proposed to simulate high-resolution data and use it as a ground truth model that is based on the pCLE acquisition physics. However, pCLE images are reconstructed from irregularly distributed fibre signals, and grid-based Convolutional Neural Networks are not designed to take irregular data as input. To alleviate this problem, I designed a new trainable layer that embeds Nadaraya- Watson regression. Finally, I proposed a novel blind super-resolution approach by deploying unsupervised zero-shot learning accompanied by a down-sampling kernel crafted for pCLE. I evaluated these new methods in two ways: a robust image quality assessment and a perceptual quality test assessed by clinical experts. The results demonstrate that the proposed super-resolution pipelines are superior to the current reconstruction algorithm in terms of image quality and clinician preference

    Deep learning for intracellular particle tracking and motion analysis

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    Structure-aware image denoising, super-resolution, and enhancement methods

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    Denoising, super-resolution and structure enhancement are classical image processing applications. The motive behind their existence is to aid our visual analysis of raw digital images. Despite tremendous progress in these fields, certain difficult problems are still open to research. For example, denoising and super-resolution techniques which possess all the following properties, are very scarce: They must preserve critical structures like corners, should be robust to the type of noise distribution, avoid undesirable artefacts, and also be fast. The area of structure enhancement also has an unresolved issue: Very little efforts have been put into designing models that can tackle anisotropic deformations in the image acquisition process. In this thesis, we design novel methods in the form of partial differential equations, patch-based approaches and variational models to overcome the aforementioned obstacles. In most cases, our methods outperform the existing approaches in both quality and speed, despite being applicable to a broader range of practical situations.Entrauschen, Superresolution und Strukturverbesserung sind klassische Anwendungen der Bildverarbeitung. Ihre Existenz bedingt sich in dem Bestreben, die visuelle Begutachtung digitaler Bildrohdaten zu unterstützen. Trotz erheblicher Fortschritte in diesen Feldern bedürfen bestimmte schwierige Probleme noch weiterer Forschung. So sind beispielsweise Entrauschungsund Superresolutionsverfahren, welche alle der folgenden Eingenschaften besitzen, sehr selten: die Erhaltung wichtiger Strukturen wie Ecken, Robustheit bezüglich der Rauschverteilung, Vermeidung unerwünschter Artefakte und niedrige Laufzeit. Auch im Gebiet der Strukturverbesserung liegt ein ungelöstes Problem vor: Bisher wurde nur sehr wenig Forschungsaufwand in die Entwicklung von Modellen investieret, welche anisotrope Deformationen in bildgebenden Verfahren bewältigen können. In dieser Arbeit entwerfen wir neue Methoden in Form von partiellen Differentialgleichungen, patch-basierten Ansätzen und Variationsmodellen um die oben erwähnten Hindernisse zu überwinden. In den meisten Fällen übertreffen unsere Methoden nicht nur qualitativ die bisher verwendeten Ansätze, sondern lösen die gestellten Aufgaben auch schneller. Zudem decken wir mit unseren Modellen einen breiteren Bereich praktischer Fragestellungen ab

    Review : Deep learning in electron microscopy

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    Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

    RELLISUR: A Real Low-Light Image Super-Resolution Dataset

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    The RELLISUR dataset contains real low-light low-resolution images paired with normal-light high-resolution reference image counterparts. This dataset aims to fill the gap between low-light image enhancement and low-resolution image enhancement (Super-Resolution (SR)) which is currently only being addressed separately in the literature, even though the visibility of real-world images is often limited by both low-light and low-resolution. The dataset contains 12750 paired images of different resolutions and degrees of low-light illumination, to facilitate learning of deep-learning based models that can perform a direct mapping from degraded images with low visibility to high-quality detail rich images of high resolution

    초점 스택에서 3D 깊이 재구성 및 깊이 개선

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    학위논문 (박사) -- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2021. 2. 신영길.Three-dimensional (3D) depth recovery from two-dimensional images is a fundamental and challenging objective in computer vision, and is one of the most important prerequisites for many applications such as 3D measurement, robot location and navigation, self-driving, and so on. Depth-from-focus (DFF) is one of the important methods to reconstruct a 3D depth in the use of focus information. Reconstructing a 3D depth from texture-less regions is a typical issue associated with the conventional DFF. Further more, it is difficult for the conventional DFF reconstruction techniques to preserve depth edges and fine details while maintaining spatial consistency. In this dissertation, we address these problems and propose an DFF depth recovery framework which is robust over texture-less regions, and can reconstruct a depth image with clear edges and fine details. The depth recovery framework proposed in this dissertation is composed of two processes: depth reconstruction and depth refinement. To recovery an accurate 3D depth, We first formulate the depth reconstruction as a maximum a posterior (MAP) estimation problem with the inclusion of matting Laplacian prior. The nonlocal principle is adopted during the construction stage of the matting Laplacian matrix to preserve depth edges and fine details. Additionally, a depth variance based confidence measure with the combination of the reliability measure of focus measure is proposed to maintain the spatial smoothness, such that the smooth depth regions in initial depth could have high confidence value and the reconstructed depth could be more derived from the initial depth. As the nonlocal principle breaks the spatial consistency, the reconstructed depth image is spatially inconsistent. Meanwhile, it suffers from texture-copy artifacts. To smooth the noise and suppress the texture-copy artifacts introduced in the reconstructed depth image, we propose a closed-form edge-preserving depth refinement algorithm that formulates the depth refinement as a MAP estimation problem using Markov random fields (MRFs). With the incorporation of pre-estimated depth edges and mutual structure information into our energy function and the specially designed smoothness weight, the proposed refinement method can effectively suppress noise and texture-copy artifacts while preserving depth edges. Additionally, with the construction of undirected weighted graph representing the energy function, a closed-form solution is obtained by using the Laplacian matrix corresponding to the graph. The proposed framework presents a novel method of 3D depth recovery from a focal stack. The proposed algorithm shows the superiority in depth recovery over texture-less regions owing to the effective variance based confidence level computation and the matting Laplacian prior. Additionally, this proposed reconstruction method can obtain a depth image with clear edges and fine details due to the adoption of nonlocal principle in the construct]ion of matting Laplacian matrix. The proposed closed-form depth refinement approach shows that the ability in noise removal while preserving object structure with the usage of common edges. Additionally, it is able to effectively suppress texture-copy artifacts by utilizing mutual structure information. The proposed depth refinement provides a general idea for edge-preserving image smoothing, especially for depth related refinement such as stereo vision. Both quantitative and qualitative experimental results show the supremacy of the proposed method in terms of robustness in texture-less regions, accuracy, and ability to preserve object structure while maintaining spatial smoothness.Chapter 1 Introduction 1 1.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 2 Related Works 9 2.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2 Principle of depth-from-focus . . . . . . . . . . . . . . . . . . . . 9 2.2.1 Focus measure operators . . . . . . . . . . . . . . . . . . . 12 2.3 Depth-from-focus reconstruction . . . . . . . . . . . . . . . . . . 14 2.4 Edge-preserving image denoising . . . . . . . . . . . . . . . . . . 23 Chapter 3 Depth-from-Focus Reconstruction using Nonlocal Matting Laplacian Prior 38 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.2 Image matting and matting Laplacian . . . . . . . . . . . . . . . 40 3.3 Depth-from-focus . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 3.4 Depth reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . 47 3.4.1 Problem statement . . . . . . . . . . . . . . . . . . . . . . 47 3.4.2 Likelihood model . . . . . . . . . . . . . . . . . . . . . . . 48 3.4.3 Nonlocal matting Laplacian prior model . . . . . . . . . . 50 3.5 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 3.5.2 Data configuration . . . . . . . . . . . . . . . . . . . . . . 55 3.5.3 Reconstruction results . . . . . . . . . . . . . . . . . . . . 56 3.5.4 Comparison between reconstruction using local and nonlocal matting Laplacian . . . . . . . . . . . . . . . . . . . 56 3.5.5 Spatial consistency analysis . . . . . . . . . . . . . . . . . 59 3.5.6 Parameter setting and analysis . . . . . . . . . . . . . . . 59 3.6 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 Chapter 4 Closed-form MRF-based Depth Refinement 63 4.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 4.2 Problem statement . . . . . . . . . . . . . . . . . . . . . . . . . . 65 4.3 Closed-form solution . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.4 Edge preservation . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.5 Texture-copy artifacts suppression . . . . . . . . . . . . . . . . . 73 4.6 Experimental results . . . . . . . . . . . . . . . . . . . . . . . . . 76 4.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 Chapter 5 Evaluation 82 5.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 5.2 Evaluation metrics . . . . . . . . . . . . . . . . . . . . . . . . . . 83 5.3 Evaluation on synthetic datasets . . . . . . . . . . . . . . . . . . 84 5.4 Evaluation on real scene datasets . . . . . . . . . . . . . . . . . . 89 5.5 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 5.6 Computational performances . . . . . . . . . . . . . . . . . . . . 93 Chapter 6 Conclusion 96 Bibliography 99Docto
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