Image restoration using HOS and the Radon transform

The authors propose the use of higher-order statistics (HOS) to study the problem of image restoration. They consider images degraded by linear or zero phase blurring point spread functions (PSF) and additive Gaussian noise. The complexity associated with the combination of two-dimensional signal processing and higher-order statistics is reduced by means of the Radon transform. The projection at each angle is an one-dimensional signal that can be processed by any existing 1-D higher-order statistics-based method. They apply two methods that have proven to attain good one-dimensional signal reconstruction, especially in the presence of noise. After the ideal projections have been estimated, the inverse Radon transform gives the restored image. Simulation results are provided.Peer ReviewedPostprint (published version

Improved techniques for bispectral reconstruction of signals

Higher order cumulants and spectra have found a variety of uses in many areas of digital signal processing. The third order spectrum, or bispectrum, is of specific interest to researchers because of some of its properties. The Bispectrum is defined as the fourier transform of the third order cumulant se quence for stochastic processes, and as a triple product of fourier transforms for deterministic signals. In the past, bispectral analysis has been used in applications such as identification of linear filters, quadratic phase coupling problems and detection of deviations from normality. This work is aimed at developing techniques for reconstructing deterministic signals in noise us ing the bispectrum. The bispectrum is zero for many noise processes, and is insensitive to linear phase shifts. The main motivation of this work is to exploit these properties of bispectrum that are potentially useful in signal re covery. The existing bispectral recovery techniques are discussed in the signal reconstruction frame work and their main limitation in handling noisy de terministic signals is brought out. New robust reconstruction procedures are provided in order to use bispectrum in such cases. The developed algorithms are tested over a range of simulated applications to bring out their robustness in handling both deterministic and stochastic signals. The new techniques are compared with existing bispectral methods in various problems. This thesis also discusses some of the tradeoffs involved in using bispectrum based reconstruction approaches against non-bispectral methods

Blur identification and restoration of images of coronary microvessel

The objective of this research was to identify the blur characteristics of the blurred images of the rat coronary microvessel, and the information of the blur characteristics was used to restore the blurred images. The blur characteristics were analyzed by using the image power cepstrum. The Wiener filter was implemented to restore the images. There were two types of point spread functions proposed and studied for the restoration. They were: defocus blur PSF and motion blur PSF. The images were transferred from HP A900 system to an AVS workstation. The images were processed and manipulated by the AVS, and showed significant improvement in quality. Blur characteristics which were similar to the motion blur were found in all the images. The motion blur PSF did not show much effectiveness in the restoration process. No defocus blur or motion blur characteristics appeared on the cepstrum of the microvessel images, suggesting that the strobe technique was capable of acquiring stationary coronary microvessel images

Toward single particle reconstruction without particle picking: Breaking the detection limit

Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method for biological macromolecules. In a cryo-EM experiment, the microscope produces images called micrographs. Projections of the molecule of interest are embedded in the micrographs at unknown locations, and under unknown viewing directions. Standard imaging techniques first locate these projections (detection) and then reconstruct the 3-D structure from them. Unfortunately, high noise levels hinder detection. When reliable detection is rendered impossible, the standard techniques fail. This is a problem especially for small molecules, which can be particularly hard to detect. In this paper, we propose a radically different approach: we contend that the structure could, in principle, be reconstructed directly from the micrographs, without intermediate detection. As a result, even small molecules should be within reach for cryo-EM. To support this claim, we setup a simplified mathematical model and demonstrate how our autocorrelation analysis technique allows to go directly from the micrographs to the sought signals. This involves only one pass over the micrographs, which is desirable for large experiments. We show numerical results and discuss challenges that lay ahead to turn this proof-of-concept into a competitive alternative to state-of-the-art algorithms

Bispectral reconstruction of speckle-degraded images

The bispectrum of a signal has useful properties such as being zero for a Gaussian random process, retaining both phase and magnitude information of the Fourier transform of a signal, and being insensitive to linear motion. It has found applications in a wide variety of fields. The use of these properties for reducing speckle in coherent imaging systems was investigated. It was found that the bispectrum could be used to restore speckle-degraded images. Coherent speckle noise is modeled as a multiplicative noise process. By using a logarithmic transformation, this speckle noise is converted to a signal independent, additive process which is close to Gaussian when an integrating aperture is used. Bispectral reconstruction of speckle-degraded images is performed on such logarithmically transformed images when we have independent multiple snapshots

Restoration of Images Taken Through a Turbulent Medium

This thesis investigates the problem of how information contained in multiple, short exposure images of the same scene taken through (and distorted by) a turbulent medium (turbulent atmosphere or moving water surface) may be extracted and combined to produce a single image with superior quality and higher resolution. This problem is generally termed image restoration. It has many applications in fields as diverse as remote sensing, military intelligence, surveillance and recognition at a long distance, and other imaging problems which suffer from turbulent media, including e.g. the atmosphere and moving water surface. Wide-area/near-to-ground imaging (through atmosphere) and water imaging are the two main focuses of this thesis. The central technique used to solve these problems is speckle imaging, which is used to process a large number of images of the object with short exposure times such that the turbulent effect is frozen in each frame. A robust and efficient method using the bispectrum is developed to recover an almost diffraction-limited sharp image using the information contained in the captured short exposure images. Both the accuracy and the potential of these new algorithms have been investigated. Motivated by the lucky imaging technique which was used to select superior frames for astronomical imaging application, a new and more efficient technique is proposed. This technique is called lucky region, and it is aimed at selecting image regions with high quality as opposed to selecting a whole image as a lucky image. A new algorithm using bicoherence is proposed for lucky region selection. Its performance, as well as practical factors that may affect the performance, are investigated both theoretically and empirically. To further improve the quality of the recovered clean image after the speckle bispectrum processing, we also investigate blind deconvolution. One of the original contributions is to use natural image sparsity as a prior knowledge for the turbulence image restoration problem. A new algorithm is proposed and its performance is validated experimentally. The new methods are extended to the case of water imaging: restoration of images distorted by moving water waves. It is shown that this problem can be effectively solved by techniques developed in this thesis. Possible practical applications include various forms of ocean observation

Advancements of MultiRate Signal processing for Wireless Communication Networks: Current State Of the Art

With the hasty growth of internet contact and voice and information centric communications, many contact technologies have been urbanized to meet the stringent insist of high speed information transmission and viaduct the wide bandwidth gap among ever-increasing high-data-rate core system and bandwidth-hungry end-user complex. To make efficient consumption of the limited bandwidth of obtainable access routes and cope with the difficult channel environment, several standards have been projected for a variety of broadband access scheme over different access situation (twisted pairs, coaxial cables, optical fibers, and unchanging or mobile wireless admittance). These access situations may create dissimilar channel impairments and utter unique sets of signal dispensation algorithms and techniques to combat precise impairments. In the intended and implementation sphere of those systems, many research issues arise. In this paper we present advancements of multi-rate indication processing methodologies that are aggravated by this design trend. The thesis covers the contemporary confirmation of the current literature on intrusion suppression using multi-rate indication in wireless communiquE9; networks