Digital fluorescence microscopy is commonly used to track individual proteins and their dynamics in living cells. However, extracting molecule-specific information from fluorescence images is often limited by the noise and blur intrinsic to the cell and the imaging system. Here we discuss a method called "model-convolution," which uses experimentally measured noise and blur to simulate the proc...

Image restoration is a significant process commonly applied in many research fields. In particular, image deconvolution algorithms play a very important role in the research methodology in astrophysics, where recorded images are frequently submitted to deconvolution processes. In this paper, we introduce a novel image deconvolution algorithm that is competitive in terms of the restored image qu...

The received signal in ultrasonic pulse-echo inspection can be modeled as a convolution between an impulse response and the reflection sequence, which is the impulse characteristic of the inspected object. Deconvolution aims at approximately inverting this process to improve the time resolution so that the overlap between echoes from closely spaced reflectors becomes small. This paper presents ...

This thesis considers the problem of restoring an image distorted by a linear spatially invariant point spread function PSF and corrupted by noise A principle aim is to develop techniques that are practical and require a minimum amount of prior knowledge The process of deconvolution is attempted using least squares and maximum likelihood iterative algorithms A survey of the literature introduce...

Ringing and noise amplification are the most dominant artifacts in image deconvolution. These artifacts can be reduced by introducing image prior into the deconvolution process. A regularization weighting factor can control strength of the regularization. Ringing and noise can be reduced significantly with the strong weighting factor, but details can be lost. We propose a nonblind image deconvo...

A novel multisensor approach to deconvolution is developed. This theory circumvents the ill-posedness inherent in convolution equations by overdetermining the input signal by a multichannel system of convolvers f ig, chosen so that any information lost by one channel is retained by another. The deconvolution problem is then solved by constructing \deconvolvers" that allow us to construct the Di...

Thanks to its ability to yield functionally-based information, the SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. Nevertheless, due to the imaging process, SPECT images are blurred and consequently their interpretation by the clinician is often diicult. In order to improve the spatial resolution of these images and then to facilitate their interpr...

Identification of acoustic sources is a great deal and the most important problem emerging in biomedical applications and nondestructive testing. We present the basics of time reversal Acoustic Emission (AE) principle and point out the advantages of AE nondestructive testing for localization and classification of acoustic sources. This approach provides the tool for the defect localization by m...

This paper proposes a new fast and effective method of image restoration to improve the resolution of SEM images. In our approach, image resolution is improved by deconvolution with the point spread function modeled as the intensity distribution of the electron beam at the specimen's surface. The beam intensity distribution under each imaging condition is estimated by electro-optical simulation...

In this work, we propose a new approach for efficient edgepreserving image deconvolution. Our algorithm is based on a novel type of explicit image filter guided filter. The guided filter can be used as an edge-preserving smoothing operator like the popular bilateral filter, but has better behaviors near edges. We propose an efficient iterative algorithm with the decouple of deblurring and denoi...