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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 B = S ∗ K {\displaystyle B=S*K} (where * represents convolution ).
Any blurred image can be given as input to blind deconvolution algorithm, it can deblur the image, but essential condition for working of this algorithm must not be violated as discussed above. In the first example (picture of shapes), recovered image was very fine, exactly similar to original image because L > K + N.
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. [1] [2]
From left: Original image, blurred image, image deblurred using Wiener deconvolution. 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 ...
The improvement to image quality is often attractive, since the same validation issues are present even for un-enhanced images. For deconvolution to be effective, all variables in the image scene and capturing device need to be modeled, including aperture , focal length , distance to subject, lens, and media refractive indices and geometries.
An example of an image blurred using a box blur. A box blur (also known as a box linear filter) is a spatial domain linear filter in which each pixel in the resulting image has a value equal to the average value of its neighboring pixels in the input image. It is a form of low-pass ("blurring") filter.