All microscope imaging is largely affected by inherent resolution limitations because of out-of-focus light and diffraction effects. The traditional approach to restoring the image resolution is to use a deconvolution algorithm to “invert” the effect of convolving the volume with the point spread function. However, these algorithms fall short in several areas such as noise amplification and stopping criterion. In this paper, we try to reconstruct an explicit volumetric representation of the fluorescence density in the sample and fit a neural network to the target z-stack to properly minimize a reconstruction cost function for an optimal result. Additionally, we do a weighted sampling of the point spread function to avoid unnecessary computations and prioritize non-zero signals. In a baseline comparison against the Richardson-Lucy method, our algorithm outperforms RL for images affected with high levels of noise.

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An iterative solution to the problem of blind image deconvolution is presented whereby a previous image estimate is explicitly used in the new image estimation process. The previous image is pre-filtered using an adaptive, non-parametric stabilizing function that is updated based on a current error estimate. This function is experimentally shown to dramatically benefit the convergence rate for the a priori restoration case. Noise propagation from one iteration to the next is reduced by the use of a second, regularizing operator, resulting in a hybrid iteration technique. Further, error terms are developed that shed new light on the error propagation properties of this method and others by quantifying the extent of noise and regularization error propagation. Optimal non-parametric, frequency adaptive stabilizing and regularization functions are then derived based on this error analysis. / Thesis / Master of Engineering (ME)

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This paper focuses on improving speckle reduction and denoising performance in ultrasound images by leveraging neural networks. A dataset of simulated ultrasound images was created using Field-II simulation software based on CT scan images, to create clean and noisy image pairs. Various Convolutional Neural Network models based on U-Net and generative adversarial networks were developed and tested. Peak Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index Measurement (SSIM) were used as metrics along with qualitative assessment. Results show that our tuned U-Net generator network outperformed traditional filtering such as Lee and BM3D.

<p dir="ltr">The past two decades have seen photography shift from the hands of professionals to that of the average smartphone user. However, fitting a camera module in the palm of your hand has come with its own cost. The reduced sensor size, and hence the smaller pixels, has made the image inherently noisier due to fewer photons being captured. To compensate for fewer photons, we can increase the exposure of the camera but this may exaggerate the effect of hand shake, making the image blurrier. The presence of both noise and blur has made the post-processing algorithms necessary to produce a clean and sharp image. </p><p dir="ltr">In this thesis, we discuss various methods of deblurring images in the presence of noise. Specifically, we address the problem of photon-limited deconvolution, both with and without the underlying blur kernel being known i.e. non-blind and blind deconvolution respectively. For the problem of blind deconvolution, we discuss the flaws of the conventional approach of joint estimation of the image and blur kernel. This approach, despite its drawbacks, has been the go-to method for solving blind deconvolution for decades. We then discuss the relatively unexplored kernel-first approach to solving the problem which is numerically stable than the alternating minimization counterpart. We show how to implement this framework using deep neural networks in practice for both photon-limited and noiseless deconvolution problems. </p>

Treating cancer is difficult as the disease is complex and drug responses often depend on the patient's characteristics. Precision medicine aims to solve this by selecting individualized treatments. Since this involves the analysis of large datasets, machine learning can be used to make the drug selection process more efficient. Traditionally, such models utilize bulk gene expression data. However, this potentially masks information from small cell populations and fails to address tumor heterogeneity. Therefore, this thesis applies data deconvolution methods to bulk gene expression data and estimates the corresponding cell type-specific gene expression profiles. This "increases" the resolution of the input data for the drug response prediction. A hold-out dataset, LODOCV and LOCOCV were used for the evaluation of this approach. Furthermore, all results are compared against a baseline model, which was trained on bulk data. Overall, the accuracy of the cell type-specific model did not show an improvement compared to the bulk model. It also prioritizes information from bulk samples, which makes the additional data unnecessary. The robustness of the cell type-specific model is slightly lower than that of the bulk model. Note, that these outcomes are not necessarily due to a flaw in the underlying concept, but may be connected to poor deconvolution results as the same reference matrix was used for the deconvolution of all bulk samples regardless of the cancer type or disease.