Browsing by Subject "Deblurring"
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Item Open Access Deblurring text images affected by multiple kernels(2018-06) Dizdarer, TolgaImage deblurring is one of the widely studied and challenging problems in image recovery. It is an estimation problem dealing with restoration of a linearly transformed image that is additional disturbed with noise. In our research, we propose a new method to solve deblurring problems on text images a ected by multiple kernels. In our approach we focus speci cally on almost binary images that have speci c intensity structures. First, we propose a non-convex non-blind deblurring model and provide an e cient algorithm that can restore a text-like image when the blurring kernel is known. Then we provide our alternate setting, the semi-blind problem, where a kernel is determined as a linear combination of multiple kernels. We propose how one can attack the deblurring problem by using dictionaries that are constructed using any prior information about the kernel. We propose a semi-blind deblurring model that can estimate optimal kernel using the elements of the dictionary. We consider a unique algorithm structure that favors regularizing the iterations through scaled parameter values and argue the advantages of this approach. Lastly, we consider some speci c problems that are commonly used in the literature where one can utilize our alternate problem setting. We argue how one can construct a dictionary that can maximize the utility gained by the prior information regarding the blurring process and present the performance of our model in such cases.Item Open Access Image deconvolution methods based on fourier transform phase and bounded energy(2018-08) Yorulmaz, OnurWe developed deconvolution algorithms based on Fourier transform phase and bounded energy. Deconvolution is a major area of study in image processing applications. In general, restoration of original images from noisy filtered observation images is an ill-posed problem. We use Fourier transform phase as a constraint in developed image recovery methods. The Fourier phase information is robust to noise, which makes it suitable as a frequency domain constraint. One of our focus is microscopy images where the blur is caused by slight disturbances of the focus. Because of the symmetrical optical parameters, it may be assumed that the Point Spread Function (PSF) is symmetrical. This symmetry of PSF results in zero phase distortion in the Fourier transform coefficients of the original image. Since the convolution leads to multiplication in Fourier domain, we assume that the Fourier phase of some of the frequencies of observed image around the origin represents the Fourier phase of the original image in the same set of frequencies. Therefore the Fourier transform phases of the original image can be estimated from the phase of the observed image and this information can be used as a Fourier domain constraint. In order to complete the algorithm, we also use a Total Variation (TV) reduction based regularization in spatial domain. We embed the proposed Fourier phase relation and spatial domain regularization as additional constraints in well-known blind Ayers-Dainty deconvolution method. Another problem we focused on is the restoration of highly blurry Magnetic Particle Imaging (MPI) applications. In this study we developed a standalone iterative algorithm. The algorithm again relies on the symmetry property of the MPI PSF. The phase estimates of the true image are obtained from the observed image. In this case we employ an `1 projection based regularization algorithm. The `1 projection reduces the small coefficients to zero which is suitable for MPI application because the contrast between foreground and background is sufficiently large by nature. Finally, a more general restoration algorithm is developed for deconvolution of non-symmetrical filters. The algorithm uses the known Fourier phase properties of the PSF in order to estimate the Fourier transform phase of the original image. We also update the estimated Fourier transform magnitudes iteratively using the knowledge of observed image and the PSF. A TV reduction based regularization method completes the algorithm in spatial domain. Simulations and experimental results show that the proposed algorithm outperforms the Wiener filter. We also conclude that the addition of estimate of Fourier transform phase is useful in any deconvolution method.Item Open Access Parameter robustness analysis of system function reconstruction and a novel deblurring network for magnetic particle imaging(2020-12) Arol, Abdullah ÖmerMagnetic Particle Imaging (MPI) is a novel medical imaging modality that can provide excellent sensitivity, contrast and resolution for imaging the spatial distribution of superparamagnetic iron oxide nanoparticles by utilizing their nonlinear magnetization responses. System function reconstruction (SFR) and x-space reconstruction are the two main image reconstruction approaches in MPI. SFR requires time-consuming calibration measurements, which need to be repeated whenever there is a change in scanning parameters or the nanoparticle. In the first part of this thesis, the effects of using mismatched parameters during calibration measurements and imaging in SFR are investigated. Through numerical simulations, MPI signals gathered with different scanning parameters are used for reconstructing images to analyze the effects of parameter changes in image quality in SFR. In contrast to the SFR approach, standard x-space reconstruction does not require calibration measurements. However, the reconstructed images are blurred by the point spread function of the system. In the second part of this thesis, a new learning-based approach is proposed to improve the image quality in x-space reconstructed images. The proposed method learns an end-to-end mapping between the x-space reconstructed blurred images and the underlying nanoparticle distributions. By using numerical simulations, it is shown that the blurring in x-space reconstruction can be significantly reduced with the proposed method.Item Open Access Restoration of space-variant global blurs caused by severe camera movements and coordinate distortions(IOP Science, 1998) Özaktaş, H.; Pınar, M. Ç.; Akgül, M.We show that a broad class of image recovery problems where an object undergoing an arbitrary two-dimensional, time- and space-variant, non-separable, nonlinear global coordinate distortion, is imaged for a certain duration, can be formulated as a system of linear inequalities. Since the system of inequalities arising in this context can be solved efficiently, our approach yields an effective method for solving this class of image recovery problems. A novel step size policy is introduced to accelerate the parallel surrogate constraint algorithm employed. The approach is illustrated by recovering an image severely blurred by the combined effects of translational and rotational motion and elliptic scaling.