First of all beware of the difference between image sharpening and highpass filtering. The former is actually a high frequency amplified image, while the latter removes the low frequency and completely eliminates the DC component.
So assuming that you indeed wanted to remove low frequency content by appling a highpass filter, then your output will be a ghost like image with black background that shows image edges.
Highpass filtering can produce out of range values, compared to the original valid range. This can be corrected with the following:
Assuming valid data range as [0,1], then the following assures that it will remain in [0,1] again:
x[m,n] = (x[m,n] - x_min)/(x_max-xmin)
Assuming valid data range as [0,255], then the following assures that it will remain in [0,255] again:
x[m,n] = 255*(x[m,n] - x_min)/(x_max-xmin)
More generally to map a data range from [a,b], into [c,d], the following mapping can be used:
x[m,n] = (d-c)*(x[m,n] - a)/(b-a) + c
Note that a range correction may not always yield a best looking image. You might as well need to adjust distribution of intensities in nonlinear ways to pop out requested features...