Contrast enhancement algorithms for images have important applications in many fields, especially in medical images because the visual inspection of medical images is necessary in the diagnosis of many diseases. Due to their own and imaging conditions, the image contrast of the medical image is very low. Therefore, a great deal of research has been carried out in this area. This enhancement algorithm generally follows certain visual principles. It is well known that the human eye is sensitive to high-frequency signals (at the edges, etc.). Although information details are often high-frequency signals, they are often embedded in a large number of low-frequency background signals, so that their visual visibility is reduced. Therefore, the appropriate increase of high-frequency part of the visual effects can be improved and in favor of diagnosis.
In this regard, the traditional linear contrast pull-up and histogram equalization are the most widely used global image enhancement methods. Contrast pull-up linearly adjusts the dynamic range of the image, and the histogram equalization algorithms use the cumulative histogram distribution probability to remap image data. Although these methods are simple, but did not take into account the local information. Also, global histogram equalization (GHE) is also generated so that some noise is over-emphasized.
There are two ways in which local contrast enhancement is best known. One is adaptive histogram equalization (AHE), and the other is adaptive contrast enhancement (ACE). This changes the contrast of the image but requires a lot of computation. Later someone used the bilinear linear interpolation technique to overcome this problem. FIG. 1 is a schematic diagram of schematic block image of a prior art. FIG. 2 is a schematic diagram of bilinear interpolation algorithm of the prior art. In conjunction with FIGS. 1 and 2, when the image segmentation uses local image processing, the image is first divided into several blocks, and then these fast internal mapping is calculated. In order to enhance the value of a certain pixel 10, the difference of the mapping relationship is obtained by the mapping relationship between the blocks adjacent to the block where the pixel is located. The middle block 11 in the middle of the image is processed by bilinear interpolation (referring to the block 21, the adjacent horizontal block 22, and the vertical block 23). The horizontal block 12 on both horizontal sides of the image adopts a single linearity (referring to the block 21 and the adjacent horizontal block 22). The vertical edge blocks 13 on both vertical sides of the image are processed by using single linear interpolation (reference block 21 and adjacent vertical block 23). The corner blocks 14 at the four corners of the image are not interpolated. d1 is the distance from the pixel 20 to the right edge of the adjacent horizontal block 22; d1′ is the distance from the pixel 20 to the left edge of the block 21; d2 is the distance from the pixel 20 to the intersection of the lower edges of the adjacent vertical blocks 23; d2′ is the distance from the pixel 20 to the upper edge of the block 21.
However, the bilinear interpolation technique has the following disadvantages: (1) the gray scale of the diagonal block is not referenced, and when there is a big difference between the diagonal blocks, the display image cannot make the corresponding connection. (2) image edge detection and enhancement are not done. (3) linear interpolation in the local comparison smoothing process to achieve the final comparison target slower.
Therefore, it is necessary to provide a new image local contrast enhancement method.