Enhancing the contrast of microscopic images is a crucial preprocessing step for the accurate analysis and classification of biological specimens, such as bacteria in water quality monitoring applications. In this study, we aimed to identify the most suitable contrast enhancement techniques for the task of enhancing microscopic images. The evaluated techniques include traditional methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) and histogram stretching, as well as more advanced approaches like Adaptive Gamma Correction with Weighting Distribution (AGCWD), Homomorphic Filtering, and the Fuzzy Automatic Contrast Enhancement (FACE) method. The performance of these techniques is assessed using quantitative metrics, namely mean contrast, root mean square (RMS) contrast, and local contrast ratio, along with qualitative visual assessments. A comprehensive comparative analysis is presented and discussed, focusing on the enhancement of microscopic images, particularly those featuring Escherichia coli (E. coli) bacteria. This analysis provides insights into the relative strengths and limitations of each method in the context of microscopic E. coli image enhancement