This thesis emphasizes the improvement approaches of quality on digital data to be more sharpness by two- dimensional technique. Today the availability from the improvement of Digital Image Processing is used widely in various occupations such as medical field with reading the results from CT scanning military field with the improvement of aerial map for country prevention agricultural field with leaf analysis of its disease and the educational field with the image processing and robot vision to increase its effective learning and remembering development. It can be seen that the image development through its sharpness is really crucial therefore this thesis used the Gray Scale for transforming by two-dimensional wavelet and LL Band got from this wavelet transform to be improved by histogram technique. The histogram was divided into 4 parts based on its cumulative density at 0.25 0.5 0.75 and 1 then smoothed the data by separation after that brought the results to invert wavelet which gives the new image through improved LL band. The research result revealed that the mean of the Absolute Mean Brightness Error (AMBE) was decreased while enable the image sharpness to suitably increase 80%.

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.

Since the details in quasi-homogeneous region will be degraded from a conventional global image enhancement methods such as histogram equalization. This degradation causes from the saturation of the gray levels in equalization process. Then the local histogram equalization for each quasi-homogeneous region will be used in order to improve the details in the regions. To obtain the quasi-homogeneous regions the image can be segmented by using a watershed transform. The watershed transform is based on mathematical morphology; therefore the segmented regions can be effectively separated meaningful. Two adjacent regions in images which almost similar in gray levels can be separted by the watershed transform. In the color image enhancement three spectral bands are enhanced independently. Then all the three bands are assigned to each processed image in order to produce a color composite image. By the proposed algorithm the result images show the better perception on image details. Therefore the high efficiency of image classification can be obtained by using this color image.

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 image enhancement process of a digital image is a part of digital signal processing which it plays an important role in some industrial segments for instance object classification or bio-identification satellite exploration or aerial images robot vision cancer treatment etc. These image applications are necessary to consume many resources in physical hardware and computer programming including the intelligent algorithm development of complexly advanced processing in order to enhance digital images – as well as be easy to analyse by humans. This research is aimed to present on Contrast Enhancement using Bi-Histogram Equalization via Two Dimensional Discrete Wavelet Transform (BHE2DWT) in order to compose the coefficient wavelet from other signals. At the end of the process information on the approximation band from DWT will be divided into two sub-histograms and then histograms will be independently equalized. After that resulting histograms will be decomposed by IDWT. Resulting images will be not only more displaying in high contrast than their original images but also preserve the absolute mean brightness error (AMBE) to close their original images.