Abstract: Deconvolution is a widely used strategy to mitigate the blurring and noisy degradation of hyperspectral images (HSIs) generated by the acquisition devices. This issue is usually addressed by ...
Multichannel Statistical Broadband Wavelet Deconvolution for Improving Resolution of Seismic Signals
Abstract: Many popular deconvolution methods based on Robinson's convolutional model have played an important role in improving the temporal resolution of seismic data. However, the outcomes of ...
A new publication from Opto-Electronic Advances, 10.29026/oes.2024.230020 discusses revolutionizing OCT imaging. Deconvolution, an essential method widely employed in various optical imaging ...
Deconvolution is a quantitative approach that uses the picture as an estimate of the real specimen intensity and conducts the mathematical inverse of the imaging process to generate an improved ...
Selecting the right deconvolution method to analyze the composition of complex mixtures of cells just got easier. Researchers derived clear guidelines scientists can use to determine the deconvolution ...
The growing use of intact protein mass analysis, top-down proteomics, and native mass spectrometry have created a need for improved data analysis pipelines for deconvolution of electrospray (ESI) mass ...
Reveal the hidden structures in your sample using cellSens imaging software, which includes a wide range of powerful deconvolution tools. Real-time deconvolution can be applied to live images using ...
The retrospective identification of the drug targets that underlie an observed phenotypic response is termed target deconvolution. Target deconvolution can be achieved by numerous methods including; ...
In this paper we are performing Spiking and Gap deconvolution on land 2D data; we are applying on the shot and stack data with operator length (200 ms), while in the gap deconvolution it is used ...
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