Recent advancements in ultrasound technologies, such as ultrasound localization microscopy and ultrafast ultrasound Doppler, have enabled high-definition imaging of microvasculature. However, detecting weak microbubble or blood flow signals amid strong background noise remains a challenge, particularly in deep tissues. This study aims to enhance the signal contrast of microbubble and blood flow by leveraging their distinct spatial-temporal coherence in comparison to undesired noise for robust microbubble detection and microvascular imaging. We propose to quantify the signal coherence based on similarity analysis of beamformed ultrasound microbubble/blood flow data within the plane wave imaging framework. A spatial pixel is considered more likely to be a true microbubble/blood flow signal with a higher level of similarity, which can be measured by either of the following methods: 1) spatially block-wise normalized cross-correlation between two compounded frames; 2) temporally normalized autocorrelation across multiple compounded frames; 3) normalized cross-correlation between two subsets of post-compounded frames; 4) normalized autocorrelation of the pre-compounded data across angular direction. The original microbubble/blood flow signal is then weighted by the similarity measurement on a pixel-by-pixel basis to generate images with an improved signal contrast. The robustness of the proposed methods was first demonstrated in both phantom experiments and in vivo microbubble data from kidney transplant. We further validated their feasibility in blood flow imaging without the use of microbubbles based on in vivo data of human liver and kidney. Significant contrast improvement was observed, facilitating better visualization and detection of both microbubble and noncontrast microflow signals, which indicates a great potential of the methods for improved microvascular imaging and widespread clinical translation.