441 research outputs found
Improving Blind Spot Denoising for Microscopy
Many microscopy applications are limited by the total amount of usable light
and are consequently challenged by the resulting levels of noise in the
acquired images. This problem is often addressed via (supervised) deep learning
based denoising. Recently, by making assumptions about the noise statistics,
self-supervised methods have emerged. Such methods are trained directly on the
images that are to be denoised and do not require additional paired training
data. While achieving remarkable results, self-supervised methods can produce
high-frequency artifacts and achieve inferior results compared to supervised
approaches. Here we present a novel way to improve the quality of
self-supervised denoising. Considering that light microscopy images are usually
diffraction-limited, we propose to include this knowledge in the denoising
process. We assume the clean image to be the result of a convolution with a
point spread function (PSF) and explicitly include this operation at the end of
our neural network. As a consequence, we are able to eliminate high-frequency
artifacts and achieve self-supervised results that are very close to the ones
achieved with traditional supervised methods.Comment: 15 pages, 4 figure
Microscopy image reconstruction with physics-informed denoising diffusion probabilistic model
Light microscopy is a widespread and inexpensive imaging technique
facilitating biomedical discovery and diagnostics. However, light diffraction
barrier and imperfections in optics limit the level of detail of the acquired
images. The details lost can be reconstructed among others by deep learning
models. Yet, deep learning models are prone to introduce artefacts and
hallucinations into the reconstruction. Recent state-of-the-art image synthesis
models like the denoising diffusion probabilistic models (DDPMs) are no
exception to this. We propose to address this by incorporating the physical
problem of microscopy image formation into the model's loss function. To
overcome the lack of microscopy data, we train this model with synthetic data.
We simulate the effects of the microscope optics through the theoretical point
spread function and varying the noise levels to obtain synthetic data.
Furthermore, we incorporate the physical model of a light microscope into the
reverse process of a conditioned DDPM proposing a physics-informed DDPM
(PI-DDPM). We show consistent improvement and artefact reductions when compared
to model-based methods, deep-learning regression methods and regular
conditioned DDPMs.Comment: 16 pages, 5 figure
Bayesian image restoration and bacteria detection in optical endomicroscopy
Optical microscopy systems can be used to obtain high-resolution microscopic images of tissue cultures and ex vivo tissue samples. This imaging technique can be translated for in vivo, in situ applications by using optical fibres and miniature optics. Fibred optical endomicroscopy (OEM) can enable optical biopsy in organs inaccessible by any other imaging systems, and hence can provide rapid and accurate diagnosis in a short time. The raw data the system produce is difficult to interpret as it is modulated by a fibre bundle pattern, producing what is called the “honeycomb effect”. Moreover, the data is further degraded due to the fibre core cross coupling problem. On the other hand, there is an unmet clinical need for automatic tools that can help the clinicians to detect fluorescently labelled bacteria in distal lung images. The aim of this thesis is to develop advanced image processing algorithms that can address the above mentioned problems. First, we provide a statistical model for the fibre core cross coupling problem and the sparse sampling by imaging fibre bundles (honeycomb artefact), which are formulated here as a restoration problem for the first time in the literature. We then introduce a non-linear interpolation method, based on Gaussian processes regression, in order to recover an interpretable scene from the deconvolved data. Second, we develop two bacteria detection algorithms, each of which provides different characteristics. The first approach considers joint formulation to the sparse coding and anomaly detection problems. The anomalies here are considered as candidate bacteria, which are annotated with the help of a trained clinician. Although this approach provides good detection performance and outperforms existing methods in the literature, the user has to carefully tune some crucial model parameters. Hence, we propose a more adaptive approach, for which a Bayesian framework is adopted. This approach not only outperforms the proposed supervised approach and existing methods in the literature but also provides computation time that competes with optimization-based methods
Hierarchical Bayesian sparse image reconstruction with application to MRFM
This paper presents a hierarchical Bayesian model to reconstruct sparse
images when the observations are obtained from linear transformations and
corrupted by an additive white Gaussian noise. Our hierarchical Bayes model is
well suited to such naturally sparse image applications as it seamlessly
accounts for properties such as sparsity and positivity of the image via
appropriate Bayes priors. We propose a prior that is based on a weighted
mixture of a positive exponential distribution and a mass at zero. The prior
has hyperparameters that are tuned automatically by marginalization over the
hierarchical Bayesian model. To overcome the complexity of the posterior
distribution, a Gibbs sampling strategy is proposed. The Gibbs samples can be
used to estimate the image to be recovered, e.g. by maximizing the estimated
posterior distribution. In our fully Bayesian approach the posteriors of all
the parameters are available. Thus our algorithm provides more information than
other previously proposed sparse reconstruction methods that only give a point
estimate. The performance of our hierarchical Bayesian sparse reconstruction
method is illustrated on synthetic and real data collected from a tobacco virus
sample using a prototype MRFM instrument.Comment: v2: final version; IEEE Trans. Image Processing, 200
Filter-Based Probabilistic Markov Random Field Image Priors: Learning, Evaluation, and Image Analysis
Markov random fields (MRF) based on linear filter responses are one of the most popular forms for modeling image priors due to their rigorous probabilistic interpretations and versatility in various applications. In this dissertation, we propose an application-independent method to quantitatively evaluate MRF image priors using model samples. To this end, we developed an efficient auxiliary-variable Gibbs samplers for a general class of MRFs with flexible potentials. We found that the popular pairwise and high-order MRF priors capture image statistics quite roughly and exhibit poor generative properties. We further developed new learning strategies and obtained high-order MRFs that well capture the statistics of the inbuilt features, thus being real maximum-entropy models, and other important statistical properties of natural images, outlining the capabilities of MRFs. We suggest a multi-modal extension of MRF potentials which not only allows to train more expressive priors, but also helps to reveal more insights of MRF variants, based on which we are able to train compact, fully-convolutional restricted Boltzmann machines (RBM) that can model visual repetitive textures even better than more complex and deep models.
The learned high-order MRFs allow us to develop new methods for various real-world image analysis problems. For denoising of natural images and deconvolution of microscopy images, the MRF priors are employed in a pure generative setting. We propose efficient sampling-based methods to infer Bayesian minimum mean squared error (MMSE) estimates, which substantially outperform maximum a-posteriori (MAP) estimates and can compete with state-of-the-art discriminative methods. For non-rigid registration of live cell nuclei in time-lapse microscopy images, we propose a global optical flow-based method. The statistics of noise in fluorescence microscopy images are studied to derive an adaptive weighting scheme for increasing model robustness. High-order MRFs are also employed to train image filters for extracting important features of cell nuclei and the deformation of nuclei are then estimated in the learned feature spaces. The developed method outperforms previous approaches in terms of both registration accuracy and computational efficiency
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