Deconvolution of confocal microscopy images using proximal iteration and sparse representations

We propose a deconvolution algorithm for images blurred and degraded by a Poisson noise. The algorithm uses a fast proximal backward-forward splitting iteration. This iteration minimizes an energy which combines a \textit{non-linear} data fidelity term, adapted to Poisson noise, and a non-smooth sparsity-promoting regularization (e.g $\ell_1$-norm) over the image representation coefficients in some dictionary of transforms (e.g. wavelets, curvelets). Our results on simulated microscopy images of neurons and cells are confronted to some state-of-the-art algorithms. They show that our approach is very competitive, and as expected, the importance of the non-linearity due to Poisson noise is more salient at low and medium intensities. Finally an experiment on real fluorescent confocal microscopy data is reported

Impulse Noise Removal Using Soft-computing

Image restoration has become a powerful domain now a days. In numerous real life applications Image restoration is important field because where image quality matters it existed like astronomical imaging, defense application, medical imaging and security systems. In real life applications normally image quality disturbed due to image acquisition problems like satellite system images cannot get statically as source and object both moving so noise occurring. Image restoration process involves to deal with that corrupted image. Degradation model used to train filtering techniques for both detection and removal of noise phase. This degeneration is usually the result of excess scar or noise. Standard impulse noise injection techniques are used for standard images. Early noise removal techniques perform better for simple kind of noise but have some deficiencies somewhere in sense of detection or removal process, so our focus is on soft computing techniques non classic algorithmic approach and using (ANN) artificial neural networks. These Fuzzy rules-based techniques performs better than traditional filtering techniques in sense of edge preservation

Application of the Least Squares Solutions in Image Deblurring

A new method for the reconstruction of blurred digital images damaged by separable motion blur is established. The main attribute of the method is based on multiple applications of the least squares solutions of certain matrix equations which define the separable motion blur in conjunction with known image deconvolution techniques. The key feature of the proposed algorithms is reflected in the fact that they can be used only in symbiosis with other image restoration algorithms

Image reconstruction using Wiener filtering and unsharp masking: a computer model

Research was conducted to computer model and compare the image reconstruction obtainable using Wiener filtering and unsharp masking. Wiener filtering and unsharp masking are techniques used to improve image quality and interpretation. It was demonstrated that far greater image restoration is obtained by Wiener filter than by unsharp masking because unsharp masking, unlike Wiener filtering, enhanced image noise along with the edges. A user friendly computer model, that may be used as a tutorial aid for Image Science students, incorporating Fast Fourier Transform (FFT) techniques was designed. Graphics allow the user to follow each stage of the image processing

Sparsity driven ultrasound imaging

An image formation framework for ultrasound imaging from synthetic transducer arrays based on sparsity-driven regularization functionals using single-frequency Fourier domain data is proposed. The framework involves the use of a physics-based forward model of the ultrasound observation process, the formulation of image formation as the solution of an associated optimization problem, and the solution of that problem through efficient numerical algorithms. The sparsity-driven, model-based approach estimates a complex-valued reflectivity field and preserves physical features in the scene while suppressing spurious artifacts. It also provides robust reconstructions in the case of sparse and reduced observation apertures. The effectiveness of the proposed imaging strategy is demonstrated using experimental data

Image Restoration

This book represents a sample of recent contributions of researchers all around the world in the field of image restoration. The book consists of 15 chapters organized in three main sections (Theory, Applications, Interdisciplinarity). Topics cover some different aspects of the theory of image restoration, but this book is also an occasion to highlight some new topics of research related to the emergence of some original imaging devices. From this arise some real challenging problems related to image reconstruction/restoration that open the way to some new fundamental scientific questions closely related with the world we interact with

Comparison of Computational Methods Developed to Address Depth-variant Imaging in Fluorescence Microscopy

In three-dimensional fluorescence microscopy, the image formation process is inherently depth variant (DV) due to the refractive index mismatch between imaging layers, which causes depth-induced spherical aberration (SA). In this study, we present a quantitative comparison among different image restoration techniques developed based on a DV imaging model for microscopy in order to assess their ability to correct SA and their impact on restoration. The imaging models approximate DV imaging by either stratifying the object space or image space. For the reconstruction purpose, we used regularized DV algorithms with object stratification method such as the Expectation Maximization (EM), Conjugate Gradient; Principal Component Analysis based expectation maximization (PCA-EM), and Inverse filtering (IF). Reconstructions from simulated data and measured data show that better restoration results are achieved with the DV PCA-EM method than the other DV algorithms in terms of execution time and restoration quality of the image