Introduction: Renal dynamic study is a well-established and popular test in routine practice of nuclear medicine. The test is non-invasive, rapid and unique in evaluation of the kidney function. In despite of its clinical values, the parameters derived from renogram are physiologically meaningless. That is due to the fact that renogram is not the true kidney function. From mathematical po...

Under certain conditions, multi-frame image sequences can be processed to produce images that achieve greater resolution through image registration and increased sampling. This technique, known as super-sampling, takes advantage of the spatial-temporal data available in an under-sampled imaging sequence. In this effort, the image registration is replaced by application of a fast blind deconvolu...

In this work, we propose a novel prior term for the regularization of blind deblurring methods. The proposed method introduces machine learning techniques into the blind deconvolution process. The proposed technique has sound mathematical foundations and is generic to many inverse problems. We demonstrate the usage of this regularizer within Bayesian blind deconvolution framework, and also inte...

A deconvolution error avoidance technique in Richardson-Lucy deconvolution (RLdeconv) is proposed, which is used for inversely analysing the SRAM margin variations caused by the Random Telegraph Noise (RTN). The proposed technique reduces the phase difference between the deconvoluted RTN distribution and feedback-gain in the maximum likelihood (MLE) gradient iteration cycles. This avoids an unw...

Deconvolution of the telescope Point Spread Function (PSF) is necessary for even moderate dynamic range imaging with interferometric telescopes. The process of deconvolution can be treated as a search for a model image such that the residual image is consistent with the noise model. For any search algorithm, a parameterized function representing the model such that it fundamentally separates si...

This paper considers the reconstruction of a shape from an object’s optical sectioning. To this end we describe three algorithms relying on the scalar optics model of light propagation of which the most involved is the deconvolution approach. This approach produces a sequence of deblurred images. We improve the deconvolution approach by suggesting a novel stopping criterion for the iterative pr...

This paper deals with the simultaneous identi cation of the blur and the restoration of a noisy and blurred image. We propose the use of Dirichlet distributions to model our prior knowledge about the blurring function together with smoothness constraints on the restored image to solve the blind deconvolution problem. We show that the use of Dirichlet distributions o ers a lot of exibility in in...

Radio-astronomical observations are increasingly contaminated by interference, and suppression techniques become essential. A powerful candidate for interference mitigation is adaptive spatial filtering. We study the effect of spatial filtering techniques on radio astronomical imaging. Current deconvolution procedures such as CLEAN are shown to be unsuitable to spatially filtered data, and the ...

This paper uses a new ultrasound tissue model to propose an ultrasound image deconvolution algorithm for improving the quality of ultrasound images. This model incorporates random fluctuations of the tissue signal within the received ultrasound RF echo signal, which has not been considered in the existing ultrasound imaging algorithms. To deal with the tissue image formation and noise reduction...