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.

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%.

Currently the edge image is regarded as important in the process of digital image processing. Because in the field of image processing in certain fields require lines or the edge of a complete description of the image in the photo itself. It is also essential to engineering. Most commonly used to calculate the difference like testicle to calculate the area between the point-to-point. Applied to select the coefficients to be used in combination with a new edge image clarity of the picture more. In this research will be trimming the edge blending image to compare the quality of the image obtained by the theoretical conversion web Paulette (Wavelet Transform) to demonstrate how to mix the new image it can provide the sharpness of the image the better. The assessment found that the proposed method can increase the contrast of the image. And maintaining data within the output video in its entirety