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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]
Furthermore, he described first application of the ADMM regarding the restoration of images corrupted with Poisson noise, [20] and to solve the problem of hyperspectral unmixing, a central problem in hyperspectral imaging, widely used in remote sensing. [21] In 2003, he proposed the first efficient algorithm for wavelet-based image restoration ...
Image restoration theory is grounded in two fundamental assumptions. Communication is a goal-directed activity . Communicators may have multiple goals that are not collectively compatible, but people try to achieve goals that are most important to them at the time, with reasonable cost.
Image restoration: Image restoration focuses on solving the problem = + where is the blurry image that should be restored, is the blur kernel, is the additive noise and is the original image we wish to recover. The traditional filter which is used to solve this problem is the Wiener Filter.
Digital photograph restoration uses image editing techniques to remove undesired visible features, such as dirt, scratches, or signs of aging. People use raster graphics editors to repair digital images, or to add or replace torn or missing pieces of the physical photograph. Unwanted color casts are removed and the image's contrast or ...
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.
"Bridget Jones: Mad About the Boy" star Renée Zellweger was "sick" of hearing her own voice, which prompted her to leave Hollywood for six years in 2010. During her time outside of the spotlight ...
Deep image prior is a type of convolutional neural network used to enhance a given image with no prior training data other than the image itself. A neural network is randomly initialized and used as prior to solve inverse problems such as noise reduction , super-resolution , and inpainting .