What is Local Mean Filter? The research paper "Multidirectional Scratch Detection and Restoration in Digitized Old Images" says that,


  4.1. Preprocessing. The preprocessing step aims to enhance image features along a set of chosen directions. First, image is grey-scaled
  and filtered with a sharpening filter (we subtract from the image its
  local-mean filtered version), thus eliminating the DC component.


Now, I am Googling the term "Local Mean Filter" but there is nothing available like that.

Can anyone please provide me any reference of "Local-Mean Filter"?

What filter are they using for Sharpening?
They probably just wanted to say that the image was blurred (by some local method, e.g. convolution with a Gaussian kernel) in a more scientific way. 

On the sharpening: They don't sharpen the image directly. What they do is to blur the image and the subtract the blurred version from the original. The result is the same as sharpening directly via an appropriate filter. 

Why this works

Assume the image $I$ and an blurring kernel $g$ (which could be a Gaussian kernel). In Fourier-Space, blurring the image by convolution becomes a multiplication:

$\mathscr{F}(g\star I) = \mathscr{F}(g)\mathscr{F}(I)$

with $\mathscr{F}(\cdot)$ being the Fourier-Transformation. 

Then $I - g \star I$ becomes: $\mathscr{F}(I) - \mathscr{F}(g)\mathscr{F}(I) = \mathscr{F}(I)(1-\mathscr{F}(g))$.

Since $\mathscr{F}(g)$ enhances the low-frequency components in in Fourier-Space, $1-\mathscr{F}(g)$ enhances the higher frequencies (I assume here that $\mathscr{F}(g)$ is normalized appropriately in magnitude, which can be achieved via scaling factors). So, in image-space, the resulting image is a sharpened version of $I$.