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The restoration is based on different deconvolution algorithms, that permit the recovery of objects from images that are degraded by blurring and noise. In microscopy the blurring is largely due to diffraction limited imaging by the instrument; the noise is usually photon noise .
The most common iterative algorithm for the purpose is the Richardson–Lucy deconvolution algorithm; the Wiener deconvolution (and approximations) are the most common non-iterative algorithms. High Resolution THz image is achieved by deconvolution of the THz image and the mathematically modeled THz PSF.
An example of an experimentally derived point spread function from a confocal microscope using a 63x 1.4NA oil objective. It was generated using Huygens Professional deconvolution software. Shown are views in xz, xy, yz and a 3D representation. In microscopy, experimental determination of PSF requires sub-resolution (point-like) radiating sources.
The Huygens–Fresnel principle (named after Dutch physicist Christiaan Huygens and French physicist Augustin-Jean Fresnel) states that every point on a wavefront is itself the source of spherical wavelets, and the secondary wavelets emanating from different points mutually interfere. [1] The sum of these spherical wavelets forms a new wavefront.
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.
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 ...
Huygens principle simply and elegantly explains many aspects of wave propagation, including diffraction. Longhurst: “It (Huygens theory) was not sufficient to explain in detail the departures from exactly rectilinear propagation of light which are encountered in the cases of diffraction”
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).