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.

The objectives of this study were to calculate the probability of type I error power of a test and to compare the performance of three test statistics: Z Student’s t and Welch’s t. Including the recommending the appropriate test statistic. Classification of the population according to five distribution. The ratios of mean group 1 per group 2 were three levels. The ratios of variance group 1 per group 2 were three levels. The sample sizes of two populations were nine sizes. The significance levels that to study were 0.01 and 0.05. The data were simulated by using SAS programming with 1000 replicates. Results of the research were Z test statistic could control the probability of type I error and had higher power of a test than other test statistics but this test is necessary to know the variances of the two groups. Therefore it was appropriate to apply the other test statistics i.e. Student’s t or Welch’s t. If the variances of the two groups were equal Student’s t was a more appropriate test statistic than Welch’s t. But if the variances of the two groups were not equal Welch’s t was a more appropriate. When both sample sizes were large and the population variances were equal we could use any tests since there is no difference in the performance.

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.