In this paper we present our new method of automatic control of X-ray picture gray scale stretch, noise reduction and
visual spatial resolution enhancement that improves the human visual picture analysis. The method is based on our set-theoretical
model of the image using details clustering of a class of large details with dimensions more 4 pixels and a low
dimension detail class. The last class is divided into two subclasses of distinguishable details and detectable details only.
The large detail data histogram determines a pixels volume in dark area and a data value related to histogram maximum
in the dark area. The received picture features are used as adaptation parameters for optimization of a picture global or
local automatic gray scale stretch, noise reduction and visual spatial resolution enhancement improving object
recognition. The small detail clustering into the two subclasses provides automatic visual resolution enhancement
without noise visibility increase. The developed automatic control of X-ray image improvement was took training and
was checked by processing series of objects: test patterns, various baggages, telephone sets, etc. The check results
provided a fine tune of the developed automat improving object recognition. The experimental and practical results are
discussed in the paper.