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Blind deconvolution is a well-established image restoration technique in astronomy, where the point nature of the objects photographed exposes the PSF thus making it more feasible. It is also used in fluorescence microscopy for image restoration, and in fluorescence spectral imaging for spectral separation of multiple unknown fluorophores .
Deblurring an image using Wiener deconvolution. Deblurring is the process of removing blurring artifacts from images. Deblurring recovers a sharp image S from a blurred image B, where S is convolved with K (the blur kernel) to generate B. Mathematically, this can be represented as = (where * represents convolution).
Cellular deconvolution algorithms have been applied to a variety of samples collected from saliva, [5] buccal, [5] cervical, [5] PBMC, [6] brain, [2] kidney, [1] and pancreatic cells, [1] and many studies have shown that estimating and incorporating the proportions of cell types into various analyses improves the interpretability of high ...
The Richardson–Lucy algorithm, also known as Lucy–Richardson deconvolution, is an iterative procedure for recovering an underlying image that has been blurred by a known point spread function. It was named after William Richardson and Leon B. Lucy , who described it independently.
The main cause of ringing artifacts is overshoot and oscillations in the step response of a filter.. The main cause of ringing artifacts is due to a signal being bandlimited (specifically, not having high frequencies) or passed through a low-pass filter; this is the frequency domain description.
In mathematics, Wiener deconvolution is an application of the Wiener filter to the noise problems inherent in deconvolution. It works in the frequency domain , attempting to minimize the impact of deconvolved noise at frequencies which have a poor signal-to-noise ratio .
In image processing, blind deconvolution is a deconvolution technique that permits recovery of the target scene from a single or set of "blurred" images in the presence of a poorly determined or unknown point spread function (PSF). [2] Regular linear and non-linear deconvolution techniques utilize a known PSF.
Deconvolution, on the other hand, is generally considered an ill-posed inverse problem that is best solved by nonlinear approaches. While unsharp masking increases the apparent sharpness of an image in ignorance of the manner in which the image was acquired, deconvolution increases the apparent sharpness of an image, but is based on information ...