The growing demand for scalable biomedical solutions calls for precise, programmable, and cost-effective manipulation techniques at the microscale. While mechanical and magnetic methods have been explored, they often face limitations in flexibility, biocompatibility, or scalability. Optical tweezers offer high precision, but suffer from thermal side effects and complex, expensive setups. Optoelectronic tweezers (OET), by contrast, utilize patterned light to induce dielectrophoretic forces, enabling flexible, energy-efficient, and scalable control of microscopic particles using relatively simple hardware.

To support advanced biomedical research at Delft University of Technology, a fully functional OET platform was developed from the ground up. It integrates a custom transparent photoconductive microfluidic chip with a DMD-based optical system for real-time, reconfigurable actuation. Custom-fabricated PDMS microgear robots were successfully manipulated under varying electrical and optical conditions. Using precise motion tracking and calibration, the generated dielectrophoretic forces were quantified, with peak values approaching 500 pN, and benchmarked against theoretical models and literature estimates.

This research demonstrates that complex-shaped microbots can be effectively actuated within a custom-built OET system, paving the way for future applications in automated diagnostics, single-cell manipulation, and intelligent lab-on-a-chip platforms. By combining hardware innovation with theoretical insight, this work lays a robust foundation for microscale robotics in next-generation biomedical technologies.

Forensic microtrace investigation relies on a time- and labour-intensive process of manually analysing samples via microscopy. To aid forensic experts in their investigations, an image recognition model for microtrace localisation and classification is needed. This work investigates the trace recognition accuracy that can be achieved by analysing images captured with automated microscopy through deep learning. Fibres, hairs, skin, glass and sand are pixel-wise classified in microscopy scans of tape-lift samples. As deep learning requires extensive amounts of annotated training data, pretraining is investigated to minimise the required annotation workload. ImageNet pretraining, pretraining with self-supervised learning and a sequential application of these approaches are tested. It is found that pretrained models are able to reduce the required annotated data twofold compared to models trained from scratch, while retaining the prediction accuracy. While the ImageNet-pretrained models outperform the self-supervised-pretrained models, the highest accuracy is achieved by combining the two approaches. With this, traces are recognised with a mean intersection over union of 0.56 when training on only 2.2 dm2 of annotated tape lift scans.

Super-resolution microscopy has been demonstrated to surpass the diffraction limit, delivering high-resolution images. State-of-the-art super-resolution research continually strives to improve imaging resolution using existing super-resolution technology. This thesis aims to enhance super-resolution microscopy resolution through engineered illumination and establish a solid theoretical foundation to support resolution improvements....@en

In the field of robotics, consider the following problem scenario: In a robot environment, a simple robot must push objects to reference places while figuring out which objects can be pushed, what the best manipulation strategy is, or which objects are static and cannot be pushed. The problem scenario can be decomposed into three research topics which individually have received much attention from the research community; learning object dynamics [8, 37], Navigation Among Movable Objects (NAMO) [7, 13, 21, 23] and nonprehensile pushing [2, 5, 25, 41, 42, 43]. A combination of these three topics could lead to improvements in planning, execution time, and reasoning, but it has not been explored in the literature. This thesis proposes a robot framework that combines these three research topics. This framework comprises of three key components: the hypothesis algorithm, the hypothesis graph, and the knowledge graph. The hypothesis algorithm computes a hypothesis on how to relocate an object to a new pose by computing possible action sequences given certain robot skills. In doing so, the hypothesis algorithm creates an hypothesis graph that encapsulates the structure of the action sequences and ensures the robot eventually halts. Once a hypothesis is carried out on the robot, information about the execution, such as the outcome, the prediction error, the type of controller used and other metrics, are stored in the knowledge graph. The knowledge graph is populated over time, allowing the robot to learn, for instance, object properties and then refine the hypothesis computed to increase task performance, such as success rate and execution time. A new planning algorithm is proposed that can detect a when a path is blocked by an object, the hypothesis algorithm relies on the newly proposed planner to generate action sequences and to free blocked paths. This planner extends the double tree optimised Rapidly-exploring Random Tree algorithm [7]. The planner constructs a configuration space for an object and is provided with starting- and target pose for that object. The planner then converts these poses to points in configuration space to then search for a path connecting the starting configuration to the target configuration. For the new planner, objects are initially classified as “unknown” and can later be categorized as either “movable” or “unmovable”. The object type information is then used when constructing the configuration space for the newly proposed planning algorithm. Configuration space consists of the conventional free- and unmovable- (or obstacle) space and the newly proposed unknown- and movable space. To carry out the investigation, a mobile robot in a robot environment with movable and unmovable objects is created. The robot is given a task that involves relocating a subset of the objects in the robot environment through driving and nonprehensile pushing. The task can be broken down into individual subtasks that consist of an object and a target pose. Planning for a push or drive action occurs with the newly proposed planning algorithm that, if successful, completes a given task and populates the knowledge graph with learned object information. Information that can be used to determine which objects to manipulate and what strategy performs best to manipulate a specific object. In an effort to develop a robot framework that combines these three topics, a framework is created that shows improved task execution as a result to experience gained in the robot environment. The proposed framework performs equivalent or better compared to the state-of-the-art frameworks that are specialized in only two out of three research topics [47]. It can be concluded that the framework partly combines the three topics because learning system models with a system identification module is moved to the future work section. Instead, the proposed method selects the best available control and system model combination in the set of available control and system model combinations.

The study of tumor microenvironments (TMEs) and immune cell composition in cancer, a disease characterized by uncontrolled growth and spread of tumor cells, has become increasingly important for understanding tumor progression and patient outcomes. Tools such as the TME-Analyzer enable this kind of research, but their manual workflows highlight a common problem in medical imaging: the scarcity of labeled data. This limits the efficiency and applicability of supervised learning algorithms to improve such medical image analysis tools. Self-supervised learning algorithms offer a promising alternative by learning feature representations without requiring labeled data. This thesis aims to address the issue of label scarcity by exploring the potential of self-supervised learning models for TME analysis involving the classification of individual cells in multiplex immunofluorescence (MxIF) microscopy images of triple-negative breast cancer (TNBC) tissue.

To enable the learning of feature representations from MxIF images with an arbitrary number of color channels, this thesis proposes to pre-train an encoder network on every image channel separately according to the SimCLR algorithm and perform classification of multi-channel images by feeding the concatenated feature representation outputs of every channel to a classifier network — referred to as the Siamese configuration. A hyperparameter search is conducted to optimize the SimCLR encoder’s ability to learn high-quality feature representations of individual cells in MxIF images of TNBC tissue. Upon obtaining an optimal set of hyperparameters, the effectiveness of the learned feature representations in improving label-efficiency for individual cell classification is assessed.

The results demonstrate that the proposed Siamese configuration improves the accuracy of classifying the inflammation status of TNBC tumor sections by 2.63%. Additionally, the optimal set of hyperparameters identified through the search include the use of the normalized temperature cross-entropy loss function with low temperature and an added image intensity thresholding term, as well as zoom and brightness/contrast augmentations. Furthermore, the optimized self-supervised learning model improves label-efficiency for individual cell classification, maintaining performance with only 40% of labeled data, while performance drops only when the label percentage is reduced below this threshold.

Micromachined membrane deformable mirrors (MMDMs) are commonly utilized in Adaptive Optics (AO) systems due to their relatively good performance and cost-effectiveness. However, these deformable mirrors often exhibit nonlinearity at high control magnitudes and a response that is dependent on external factors such as temperature and humidity. In order to overcome these nonidealities, AO controllers typically implement a linear proportional-integral closed-loop control. Nevertheless, if the model is inaccurate, multiple wavefront (WF) measurements are required, which slow down operations. To address these issues, this thesis proposes a novel approach based on the Direct Inverse Control (DIC) framework, which involves modeling and controlling the AO system using shallow neural networks. Specifically, the specialized learning DIC framework is employed. This approach consists of first identifying a forward model of the plant using a neural network, then placing the controller network in series with the plant one, and finally training the controller to make the overall system resemble an identity transfer function. Since the analyzed system is underdetermined, the controller loss function is augmented with a Lagrangian multiplier. This additional term also enables the regularization of the inversion process, which helps to reduce the risk of saturating actuators. The results of this study show that the proposed approach provides better modeling accuracy
than benchmarks, especially in the working ranges where nonlinearities are present. As a result, it enables faster control convergence than the state-of-the-art method when generating large-phase wavefronts. Moreover, when operating online, the DIC-based method demonstrates better stability and similar tracking abilities to Recursive Least Squares. Overall, the proposed approach provides a promising solution to the challenges associated with using MMDMs in AO systems.

Traversability estimation is a key component in autonomous driving tasks. In many applications, semantic segmentation is used to pixel-wise classify a visual scene. The pixel-wise segmented map is used to estimate the traversability of different environments. The semantic segmentation accuracy can drop if environmental conditions change. The introduction of shadow in images can cause the segmentation network to misclassify pixels, which leads to an
inaccurate traversability estimation. This inaccurate estimation could lead an autonomous vehicle to deviate from traversable paths, leaving it unable to continue or even cause accidents.

To increase the segmentation accuracy in shadow conditions, shadow removal before semantic segmentation is proposed. In this research, the supervised Dual Hierarchical Aggregation Network (DHAN) and unsupervised cycle-based Shadow Generative Adversarial Network (SGAN) are used for shadow removal before semantic segmentation with Segmentation Network (SegNet). The shadow removal networks are evaluated on two datasets, containing image triplets, consisting of shadow, shadow-free and shadow-mask images. The structural
similarity is calculated for complete images and the non-shadow regions by inverting
the shadow-masks. The networks are applied to the Cambridge-driving Labeled Video Database (CamVid) dataset to evaluate the change in segmentation accuracy. In a second set of experiments, the DHAN shadow removal network is retrained on multiple datasets containing synthetic shadows. The obtained DHAN networks are tested on shadows with increasing intensity. The retrained DHAN networks are compared to evaluate the training dataset for shadow removal to increase segmentation accuracy.

The experiments show that the DHAN increases the structural similarity of 99.8% and the SGAN for 74.5% of the datasets. After retraining on the synthetic shadow dataset, segmentation accuracy after DHAN shadow removal increases the segmentation Pixel Accuracy (PA) for a maximum of 91% and the segmentation mean Intersection over Union (mIoU) for a maximum of 88% of the images of the CamVid dataset. We conclude that segmentation accuracy increases after DHAN shadow removal if the DHAN is trained on the synthetic shadow dataset.

Counter-acting image quality degradation caused by phase aberrations through physical correction requires the phase field to be known. As imaging hardware captures real-valued intensity, defined as the wave amplitude squared, obtaining this lost phase information is known as the phase retrieval problem and is a non-linear and non-convex optimisation problem.

Literature treats this problem from two points of view: reconstruction using indirect phase sampling, and reconstruction using Fourier amplitude sampling. The former employs wavefront sensors such as the Shack-Hartmann sensor, which encodes phase gradient information in the form of the displacement of imaged spots. These methods, while fast, discard information such as interference and are limited to low-order reconstruction. The latter, also called wavefront sensorless phase retrieval, uses the full point-spread function (PSF) to obtain high-accuracy reconstruction at the cost of computational speed, number of intensity images required, or limited aberration magnitude. These two points of view have remained largely separated, but can be made compatible through a modelling technique called Shack-Hartmann diversity. This thesis explores the merging of wavefront sensorless methods with Shack-Hartmann intensity patterns to leverage more information from a single captured image frame.

Firstly, a low-order modal reconstruction technique is presented applied to phase gradient fields obtained from Fourier demodulation of a Shack-Hartmann intensity pattern. A method of minimising the amount of redundant data-points used for reconstruction is illustrated through the removal of Fourier-interpolated data to speed up performance.

Secondly, a novel extension of the Fourier demodulation technique to hexagonal Shack-Hartmann arrays is presented, allowing phase gradient extraction and modal reconstruction of hexagonal array intensity images using Fourier demodulation.

Thirdly, Shack-Hartmann diversity is used to extend intensity-based modal phase retrieval using Taylor approximation of the intensity function to Shack-Hartmann intensity patterns. This bridges the gap between wavefront sensorless methods and Shack-Hartmann intensity patterns.

Lastly, a novel hybrid method is presented for high-accuracy phase reconstruction based on applying the above intensity-based method to a single Shack-Hartmann intensity pattern with low-order pre-conditioning obtained from Fourier demodulation. The method is demonstrated to obtain highly accurate reconstruction on low-order aberrations, and better reconstruction accuracy on small-magnitude high-order aberrations with dominating large-magnitude low-order terms than traditional methods. Potential use cases are discussed, such as open-loop turbulence reconstruction for use in turbulence modelling.

Acoustic Force Spectroscopy (AFS) is a versatile tool that uses sound waves to manipulate tiny particles such as cells, bacteria, and even zebrafish embryos in microfluidic systems. This kind of acoustic tweezer is gaining increasing attention due to its high throughput capability and non-invasiveness. In addition, this device allows for parallel manipulation of bio-molecules. Therefore, it can provide statistically significant data about the DNA replication process, which is widely considered a stochastic process. Understanding the dynamics of the DNA replication process plays a vital role in developing medicine to cure diseases that are still incurable today. Most single-molecule techniques, such as optical tweezers, magnetic tweezers, and atomic force microscopy, can only manipulate a limited number of particles simultaneously. Therefore, obtaining statistically significant data with these methods is laborious, time-consuming, and expensive. This report describes the design, fabrication, simulation and characterization of an easy-tobuild and cost-effective acoustic tweezer. The trapping stiffness of this device is derived from finite element modelling and experiments. This property is used to benchmark the acoustic tweezer developed in this project against other microparticle manipulation devices found in the literature. The results show that the acoustic tweezer developed in this project provides sufficient trap stiffness for studying DNA replication.

The microscope is an essential tool for biologists. Since the late 16th century, it has given researchers a better understanding of cell processes and greatly advanced healthcare. In this century, Single molecule localization microscopy (SMLM) has revolutionized optical microscopy by breaking the optical diffraction limit. Sparsely activating emitters in a sample labeled with fluorophores, the object can be reconstructed by estimating their positions using the system point spread function (PSF). These localization algorithms are the state of the art
in optical imaging, using unbiased estimators to reach the theoretical minimum uncertainty, or Cramér-Rao lower bound (CRLB).
While SMLM works well when emitters are sparsely activated, overlap of the emitter images is inevitable for thick or densely labeled samples. When SMLM is used on such images, the estimates become biased and the algorithm cannot find the correct number of emitters. Most techniques also make a deterministic estimate and are incapable of representing the uncertainty of estimates for dense samples.
A three-dimensional, Bayesian multiple emitter fitting algorithm is constructed using reversible jump Markov chain Monte Carlo (RJMCMC). While following the structure of Bayesian multiple-emitter fitting (BAMF), novel RJMCMC moves are designed to sample the parameters. The algorithm also jumps through models, estimating the number of emitters. It asymptotically samples from the posterior, revealing uncertainties in three-dimensional imaging that other techniques are incapable of imaging.
The algorithm was tested with astigmatic and biplane imaging. It has proven capable of consistently finding the correct model when a prior on emitter intensity is used. When separating two emitters, posterior density reconstruction revealed non-Gaussian emitter position uncertainties. Upon further investigation, the posterior density was found to be multimodal, with
both modes representative of the data and indistinguishable in terms of likelihood. This shows the algorithm can quantify three-dimensional PSF degeneracy and can become a vital tool for researchers to analyze their imaging setup. We also expect it to be especially effective when combined with modulation-enhanced localization microscopy (meLM) techniques.

Ultrafast ultrasound localization microscopy (ULM) is a super-resolved vascular imaging method that provides a 10-fold improvement in resolution compared to ultrafast ultrasound Doppler imaging. Because typical ULM acquisitions accumulate large numbers of synthetic microbubble (MB) tracks over hundreds of cardiac cycles, transient hemodynamic variations such as pulsatility get averaged out. Here we introduce two independent processing methods to retrieve pulsatile flow information from MB tracks sampled at kilohertz framerates and demonstrate their potential on a simulated dataset. Our first approach filters out ULM localization grid artifacts and successfully recovers the pulsatility fraction Pf with a root mean square error of 3.3%. Our second approach relies on the derivation of the velocity distribution of MBs as observed from a stationary observer. We show that pulsatile flow gives rise to a bimodal velocity distribution with peaks indicating the maximum and minimum velocity of the cardiac cycle. Measuring the locations of these peaks, we successfully estimated Pf with an error of 5.2%. Last, we evaluated the impact of the MB localization precision σ on our ability to retrieve the bimodal signature of a pulsatile flow. Together, our results demonstrate that pulsatility can be retrieved from high framerate ULM acquisitions and that the estimation of the pulsatility fraction improves with MB localization precision.

Mapping an environment with a Light Detection and Ranging (LiDAR) sensor through the use of a LiDAR Simultaneous Localization And Mapping (SLAM) algorithm is a powerful technology that allows for the creation of detailed 3D models. Recently various LiDAR sensors have been developed based on Micro-Electro-Mechanical System (MEMS) technology. These LiDARs are very low cost and considerably smaller than conventional LiDARs. They also often incorporate other sensors such as Inertial Measurement Unit (IMU)s and cameras into the same device.
Performing LiDAR SLAM with MEMS based LiDAR is challenging due to the short range, the smaller Field of View (FOV) and the sensitivity to ambient light of MEMS based LiDAR. In this thesis the objective is to reduce the effect of these factors when doing LiDAR SLAM by incorporating IMU measurements into the position estimation of the sensor.
A graph based positioning approach is proposed to achieve tight coupling of the IMU sensor and LiDAR position estimates. The method is made more robust by incorporating an outlier detection mechanism that reduces the influence of wrong LiDAR position estimates caused by insufficient points in the LiDAR FOV or by ambient light disturbance.
The method was built in ROS and implemented on the Intel ® L515 sensor. The performance is evaluated in indoor situations with varying presence of ambient sunlight and where room size approaches the maximum limit of the sensor range. The algorithm achieves lower drift than the current state of the art for the Intel ® L515. The algorithm especially achieves altitude drift reduction and increases robustness to outliers in the LiDAR positioning.

Unmanned Ground Vehicle (UGV) navigation in unstructured off-road environments can benefit from accurate traversability estimation. Often, experiments with UGVs use semantic segmentation networks for visual scene understanding. Based on the pixel-wise classification of a semantic segmentation network, the UGV can distinguish traversable from non-traversable terrain. However, it is still an open challenge to design a model which is able to accurately estimate traversability in a variety of environments. Variation in terrain characteristics and different levels of structuredness requires a model with a high level of generalisability. Limited generalisability will result in inaccurate traversability estimation, which in the worst-case scenario can cause the UGV to crash.

In order to overcome limited generalisability, a hybrid semantic segmentation framework is presented that can switch between different operation modes. The hybrid framework contains multiple environment-specific segmenters. For each input frame, the hybrid framework selects an environment-specific segmenter, based on a decision parameter. In this work, two hybrid frameworks containing different decision parameters are designed. The first hybrid framework contains multiple Bayesian segmenters, which quantifies prediction uncertainty in addition to the pixel-wise classification. This uncertainty quantification is obtained by Monte Carlo sampling to generate a posterior distribution of pixel class labels. The second hybrid framework consists of multiple environment-specific segmenters and autoencoders. Every segmenter has a corresponding autoencoder trained on the same environmental dataset. The output of the environment-specific autoencoder is a reconstructed image of the input image. The error between the original input image and the reconstructed image is used as a decision parameter for selecting the best performing segmenter.

We experimented with a hybrid segmentation framework and observed that it could outperform a single semantic segmentation network with a 2.6% Intersection over Union increase. The hybrid framework with the autoencoder approach resulted in a model selection precision of 99.3% on all the test images. Therefore, we can conclude that UGV navigation can benefit from a hybrid semantic segmentation framework.

Single Molecule Localization Microscopy (SMLM) has enabled researchers to breakthrough the diffraction limit and obtain nanometer resolution images of macromolecular structures. But due to the time involved in obtaining ample data for proper image, the technique is venerable to many problem including fluctuations due to thermal gradients from surrounding which cause the frames to drift. SMLM relies on the stochastic blinking of fluorophore probes. Thus drift in SMLM could be explicitly modelled as a stochastic state space process. These models could be used to perform drift correction. Two state space models are proposed relying on different properties of SMLM.

The first model utilizes shifting of underlying image structure. The state space model for this property is constructed using shift matrices. A system identification method along with image reconstruction method is also derived to form the drift compensation algorithm for this model. This algorithm is further developed to provide adequate performance within low computational time. The second model relies on the position of emitter molecules and utilizes linking or pairing of fluorophore probes in succeeding frame to obtain the output data. Drift compensation algorithm for this model is constructed using Prediction Error Methods (PEM) and Kalman (RTS) smoother. The drift correction algorithm for these two models are also bench-marked with existing algorithms to obtain insight into performance. Furthermore, other properties of these algorithms are explored using simulation dataset and recommendation are provided for improvement and further research.

High-Speed Atomic Force Microscopy is widely used for investigating biological architectures and the semiconductor industry. The main limitation comes from parachuting or the Wile E. Coyote effect. Parachuting is the phenomenon where the cantilever taps on the sample towards a steep decrease in height, but does not get adjusted for this decrease fast enough, resulting in poor imaging quality at higher scan speeds. A proof-of-concept has been modeled for an adaptive raster scanning methodology on the X and Y piezo actuators. There will be investigated how a variable scan speed can decrease the effect of parachuting and thereby improve the spatial resolution. A detection algorithm is designed to measure the parachuting and uphill events at high scanning speed as accurately and precisely as possible during the forward scan. This has been done by defining a mixed signal that multiplies the first and second derivatives of the filtered deflection signal. A variable scan speed will be applied for the backward scan. The detection algorithm turned out to have a very high repeatability of 97.1% for the uphill events, and 93.6% for the parachuting events. Its accuracy turned out to have a maximum deviation of one signal period, which has been accounted for within the controller. Implementing this for the adaptive controller results in an improvement in both resolution and time efficiency. The adaptive controller is up to 9.5 times more accurate and time efficient compared to conventional methods.

Optical microscopes are fundamentally limited to a resolution of several hundreds of nanometers by the diffraction of light. Single-molecule localization microscopy (SMLM) circumvents this limit by sparsely exciting fluorescent molecules at different time instances. Single molecules can subsequently be localized with improved precision over the diffraction limit. Due to their high frame rate, single-photon avalanche diode (SPAD) arrays are imagers that can be used for SMLM. Most SPADs have to recharge after each photon arrival. During this recharging period, the SPAD is insensitive to more photon arrivals. As a result, SPAD arrays will measure zero or one photons for each pixel in each frame, whereas scientific complementary metal-oxide semiconductor (sCMOS) imagers and electron multiplying charge-coupled devices (EMCCD) have a discrete frame output. Here, we describe the photon arrivals in the image formation model of the SPAD array as a binomial process rather than as a Poissonian process. In addition, we quantify the minimum theoretical uncertainty of single-molecule localizations using a binomial Cramér-Rao lower bound and benchmark it with simulated and experimental data. We show that if the expected photon count is larger than one for all pixels within one standard deviation of a Gaussian point spread function, the binomial CRLB gives a 46% higher theoretical uncertainty than the Poissonian CRLB. Without saturation, which is the case for most SMLM applications, the binomial CRLB model gives the same uncertainty as the Poissonian CRLB. Therefore, the binomial CRLB can be used to predict and benchmark localization uncertainty for SMLM with SPAD arrays for all practical emitter intensities.

Super-resolution microscopy methods are able to image samples with improved resolution over the diffraction limit. Single-molecule localization microscopy (SMLM) methods break the diffraction limit by sparsely activating fluorescent emitters. The resulting sparsity in the emission signal can be exploited by estimation algorithms, which enables localization with improved precision. Modulation enhanced SMLM (meSMLM) methods further increase the localization precision by combining SMLM techniques with patterned illumination. By using prior information to refine emitter position estimates, iterative meSMLM (imeSMLM) methods such as iterative MINFLUX are able to locally improve the resolution. For (me)SMLM methods, the Cramér-Rao lower bound (CRLB) is often used to assess the localization precision. The CRLB bounds the variance of arbitrary unbiased estimators from below. As the CRLB treats estimands as deterministic unknowns, a prior distribution on the estimands cannot be incorporated into the bound. Therefore, the effect of prior information on the localization precision of imeSMLM methods is not captured by the CRLB. The Van Trees inequality (VTI) is a Bayesian variant of the CRLB. Because it treats estimands as random variables with a known prior distribution, it is able to account for the effect of prior information on the estimator precision. It is therefore able to accurately bound the localization precision of imeSMLM methods from below. An imeSMLM method is considered, in which the positions of sinusoidal intensity patterns are controlled over the course of multiple iterations. Intensity minima of sinusoidal patterns are placed symmetrically around the current estimate of the emitter position, at a distance based on the localization precision of the previous iteration. This strategy balances the information content of signal photons with the need for robustness to estimation errors. Using the VTI, we derive a fundamental limit on the localization precision of imeSMLM methods that make use of standing wave illumination patterns. This limit shows that in the absence of background, the information content of signal photons increases exponentially as a function of the iteration count. Using Monte Carlo simulations, the maximally achievable localization precision for different illumination pattern placement control strategies was evaluated. The VTI allows to assess the performance of pattern placement control strategies and is therefore a promising method for optimal control of imeSMLM methods.

Structured illumination microscopy (SIM) is a kind of wide-field super resolution imaging technique which achieves the resolution improvement by exploiting the interference patterns to obtain the information out of the observable region in Fourier space. The resolution improvement in SIM is done by analyzing the obtained image and restored the observed sample with software. This process is sensitive to the measured PSF, as the PSF is the main factor to do the image deblurring and parameters estimation. Frequently, the PSF of a SIM system is obtained by fitting the recorded beads image with the Gaussian function. However, the Gaussian fitting cannot retrieve the aberration and noise in the PSF. The blind image deconvolution method is a family of algorithms which can be used to restore the object and corresponding PSF from the degraded images. The novel Tangential Iterative Projections (TIP) algorithm is a kind of blind image deconvolution algorithm proposed by Wilding et al.. This algorithm is simple, fast and robust to the noise condition. It is proved that TIP can be implemented on both the multi-frame deconvolution scenario and single-frame scenario. The aim of this thesis is to verify whether TIP algorithm can retrieve a PSF, which can be used to do the SIM image reconstruction, from the SIM data. For this purpose, a simulated SIM optical system is built in Matlab to generate the SIM images, and both the single-frame TIP and multi-frame TIP frameworks are used to do the PSF retrieval. In addition, a new constraint for the PSF which is generated based on the diffraction limit of the optical system is implemented. In order to reduce the execution time and avoid the overfitting of the algorithm, a stop criterion is introduced. The result shows both the multi-frame TIP and single-frame TIP can be used to retrieve an effective PSF from the SIM data, and the single-frame TIP is more applicable and more accuracy compared with the multi-frame TIP.

For a long time, scientists believed that it is not possible to achieve a better microscopic resolution than half the wavelength of light (~ 200 nm). During the last decades, various super-resolution fluorescence microscopy techniques have been developed, offering high-resolution imaging beyond the diffraction limit. It has been established as one of the major tools currently available for the study and analysis of biological tissues. The observation of, previously limited and unclear, cellular structures has demonstrated the great promise of super-resolution fluorescence microscopy in exemplifying biological processes at the cellular level. Inherently, single-molecule localization microscopy (SMLM) is based upon acquiring a sequence of diffraction-limited images, consisting of localizations of sparsely distributed emitting fluorophores. Next, precise and accurate estimates of the spatial coordinates of the fluorophores, placed on the structures of interest, are made. Subsequently, all recovered emitter positions from each frame are combined, and a super-resolution image is generated. One of the most crucial questions in SMLM concerns the precision with which the location of a single molecule can be determined. The Cramér–Rao lower bound (CRLB) is used to quantify the estimation performance in terms of localization precision. Recently, several modulation enhanced methods have shown resolution improvement by combining structured patterned illumination with SMLM techniques, such as MINFLUX and SIMFLUX. However, the analysis of images by SMLM and these modulation enhanced methods requires sparse activation regions to prevent overlap between active emitters. Recently, several methods have been developed with the ability to analyze higher density data, allowing partial overlap between emitters. In this work, we propose a maximum likelihood-based, modulation enhanced SMLM method able to localize multiple active emitters within a single region of interest (ROI), which we refer to as Multi- SIMFLUX. The mathematical framework of the algorithm is comprehensively built up throughout this work. The performance of the algorithm has been evaluated, and validated under a wide range of realistic imaging conditions. This involves various active-emitter densities, different emitter- and background intensities, numerous emitter configurations, a multitude of ROI-sizes and point spread function widths. As theoretically expected, our approach significantly outperforms conventional 2DGaussian SMLM with a twofold improvement factor in terms of localization precision for various emitter densities and intensities. At higher active emitter imaging densities, the extension from a single to a multi-emitter approach showed a clear improvement in recall fraction.