Deblurring

Deblurring is the process of removing blurring artifacts from images [input image say B which is blurred image which generally happens due to camera shake or some other phenomenon]. Now we want to recover Sharp Image S from blurred image which is B. Mathematically we represent B = S*K where B is blurred input image, we need to find out both sharp image S and K which is blur kernel and * is called convolution. We say that S is convolved with K to generate blurred image B, where K is the blur caused by defocus aberration, motion blur, gaussian blur or any kind of blur. So our goal is now to recover S which is Sharp image and also K and the process is known as Deblurring and some people called it Unblur too but Deblur is the correct technical word.

The blur K is typically modeled as point spread function and is convolved with a hypothetical sharp image S to get blurred Image B, where both the sharp image S (which is to be recovered) and the point spread function K are unknown. This is an example of an inverse problem. In almost all cases, there is insufficient information in the blurred image to uniquely determine a plausible original image, making it an ill-posed problem. In addition the blurred image contains additional noise which complicates the task of determining the original image. This is generally solved by the use of a regularization term to attempt to eliminate implausible solutions. This problem is analogous to echo removal in the signal processing domain. Nevertheless, when coherent beam is used for imaging, the point spread function can be modeled mathematically.[1] By proper deconvolution of the point spread function K and the blurred image B, the blurred image B can be deblurred (unblur) and the sharp image S can be recovered.