Optimal image histogram for Human eye I need to visualize some data as gray-scale images after contrast enhancement. The processing algorithm must be built on few samples, and then it will be applied on unseen images (suppose, the [min, max] range is given).

I plan to:


Use sample images to estimate actual histogram H1.
Define desired histogram H2.
Build a transformation T (look-up table) that transforms H1 into H2. 
Apply T on unseen images.


Question: How to build H2? Are there studies in regards to the optimal gray-scale histogram for Human eye? 

Below are two examples of different transformations, applied on the same image:
Human eye perception is a complicated thing, the human perception tend to fail in different situations even in large changes of gray tones, also is capable of note minimal changes in the gray levels but all depends on the shape of the figure rather than its histogram.

In some cases you can enhance an image by applying histogram equalization techniques but this is only to emphasize some gray tones in order to observe more details in an image. This is related to gray images.