Deconvolution is an image restoration technique which improves image contrast, resolution and signal to noise ratio. In modern optical microscopy and biological research deconvolution is becoming a fundamental processing step which allows for better image analysis. Deconvolution remains however a challenging task as the result depends strongly on the algorithm chosen, the parameters settings an...

Being interested by a field data application of Marchenko imaging, I identify a Gulf of Mexico 2-D line that I deem appropriate for the job. One of the main requirements of Marchenko imaging is to have deconvolved data. For this dataset and task, I use a non-minimum phase deconvolution approach in the lag-log domain: It can estimate wavelets of any length, shape and amplitude, and yields deconv...

Abstract: The cross-range resolution of forward-looking phase array radar (PAR) is limited by the effective antenna beamwidth since the azimuth echo is the convolution of antenna pattern and targets’ backscattering coefficients. Therefore, deconvolution algorithms are proposed to improve the imaging resolution under the limited antenna beamwidth. However, as a typical inverse problem, deconvolu...

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...

We propose a new model based on the deconvolutional networks and SAX discretization to learn the representation for multivariate time series. Deconvolutional networks fully exploit the advantage the powerful expressiveness of deep neural networks in the manner of unsupervised learning. We design a network structure specifically to capture the cross-channel correlation with deconvolution, forcin...

For the purpose of the ISBI HARDI reconstruction challenge 2013 and for the categories DTI and HARDI acquisitions, we reconstructed the diffusion datasets using two well established methods: a) Spherical Deconvolution Transform (SDT) [1], [2] and b) Constrained Spherical Deconvolution (CSD) [3]. The SDT is a sharpening operation which transforms the smooth diffusion ODF into a sharper fiber ODF...

In Imaging science, Image processing is any form of signal processing for which the input is an image and the output may be either an image or set of characteristics or parameters related to the image. Sometimes,the images may be corrupted. Such degradations may be either due to motion blur, noise or camera misfocus. So, a classical research area called Image Restoration came into existence. Th...

We present our contribution to the REVERB Challenge in this paper. A multi-channel speech dereverberation system combines cross-channel cancellation and spectral decomposition. The reverberation is modeled as a convolution operation in the spectral domain. Using the generalized Kullback-Leibler (KL) divergence, we decompose the reverberant magnitude spectrum into clean magnitude spectrum convol...

For the simplicity of implementation and saving of operation time, the "xed-order optimal deconvolution "lter design is appealing for engineers in signal processing from practical design perspective. In this study, a design method based on genetic algorithms is proposed to simultaneously treat with H 2 and H = optimal signal reconstruction design problem with prescribed "lter order. Genetic alg...

Gabor deconvolution has been updated and a new ProMAX module is released. The updates are: (1) a new method of spectral smoothing called hyperbolic smoothing; (2) a Gabor transform using compactly supported windows that improves run times by one to two orders of magnitude; and (3) a post-deconvolution time-variant bandpass filter whose maximum frequency tracks along a hyperbola in the time-freq...