Currently competition in terms of resolution and contrast of photos from digital cameras are widely mentioned. The higher resolution the photos contain; the more details the picture has. This fact enables the more details of pictures which also hold much more capacity of data. This also causes the electronic devices to be updated all the time to cope with those problems. Therefore the conventional devices are obsolete. This research has been developed and applied to improve digital photos with high contrast. The researcher has solved the problems by keeping the average error of the lowest results and improving heat histogram of various curves. The heat will break the histogram of the original image into n part by the concave side of the heat histogram. The part of this histogram is equal to n+1 and then smooth each other heap histogram independently. The result showed that the Tennengrade or contrast of the image is higher than the original one. However it can retain brightness errors (AMBE) as well

The sharpness of the image the current demand is increasingly high. Since many types of screens has developed a definition and high definition media support for secondary NICs modern electronica. But if we have information that is incomplete. Monitor is not working efficiently. There are various ways to improve the clarity of digital images. For example HE BBHE DSIHE and MMBEBHE have been developed to improve the image sharper and brighter. By improving the techniques for Histogram Equalization independently of each other. In this paper will present the Digital Image Enhancement via Multi-Peak Histogram Equalization (DIEMPHE) by histogram equalization into n sections as the turning point arch top and bottom of the Histogram. By each section is independent. Image results are the average brightness error is lower and the higher the intensity of the image.

Image enhancement is one of using in various digital signal processing areas. Advances in microcontrollers DSP boards and computers have been developed traditional algorithms to improve the quality of the resulting image. This paper aims to present the contrast enhancement using weighted bi-histogram equalisation. In additional this technique must use two weighted factors which is calculated by the histogram distribution ratio. Likewise an original image will be equalised by the modification of the probability density function of the gray levels. As the experimental results the contrasts of resulting images are improved for robot vision implement and human perception including absolute mean brightness error (AMBE) limitation.