In order to obtain high-quality images in a project, modern cameras with wide-bits (12, 14 or 16 bits width) are usually
used to acquire enough original information. Nevertheless, some kinds of data selection and transform processing are
necessary to display such images on PC (8 bits width). Besides, the output image should include both high general
contrast and clear details. This paper proposed a method with two major steps: for the first step is on the basis of partially
overlapped sub-block histogram equalization (POSHE), and change the way of equalizing sub-block image, separate
each sub-block recursively with different gray ranges. The second step is taking a kind of pseudo-color processing based
on HIS space to enhance the visual effects, so that the image has rich layers and consistent with human’s perception.
Experimental results show that the algorithm could keep the local details and the mean of the original brightness at the
same time, enhanced the image effectively. Considered the adaptability on different scenarios, different objectives, and a
reasonable amount of time complexity, this method could adapt the requirements of practical engineering applications.
In this paper, A segmentation model that combines techniques of curve evolution, the Mumford-Shah model and level
set method was presented, to detect the contour of object in a given image, the model can detect object whose boundary
is not necessarily defined by gradient and whose gray structure may be complicated. First we construct signed distance
function, adopted a method which based on the times that is odd or even numbers through close curve from the point
along a direction (if need, may be along several directions) to construct sign table. Then we used improved Mumford-Shah model to segment image, we consider that the object to be segmented is made up of some different gray level, it is
difficult to detect the object contour using the Mumford-Shah model, for general objects, the contour of the object is
piecewise-contour of along the edge, and the gray difference among the object points nearby the contour is little, so we
divide the curve into finite segment, compute gray average of narrow band in and out of the curve, and compute the gray
difference between the inner narrow band and outer narrow band of the curve, using improved Mumford-Shah model to
segment the object. Experiment results show that the proposed algorithm can be used to segment object without edge and
with complex gray structure, and the performance of the algorithm is satisfactory.