We have applied an advanced multi-aperture indexing photometry and sophisticated de-trending method to existing Taiwanese-American Occultation Survey (TAOS) data sets. TAOS, a wide-field ($3^{circ}{ imes}3^{circ}$) and rapid photometry (5Hz) survey, is designed to detect small objects in the Kuiper Belt. Since TAOS has fast and multiple exposures per zipper mode image, point spread function (PSF) varies in a given image. Selecting appropriate aperture among various size apertures allows us to reflect these variations in each light curve. The survey data turned out to contain various trends such as telescope vibration, CCD noise, and unstable local weather. We select multiple sets of stars using a hierarchical clustering algorithm in such a way that the light curves in each cluster show strong correlations between them. We then determine a primary trend (PT) per cluster using a weighted sum of the normalized light curves, and we use the constructed PTs to remove trends in individual light curves. After removing the trend, we can get each synthetic light curve of star that has much higher signal-to-noise ratio. We compare the efficiency of the synthetic light curves with the efficiency of light curves made by previous existing photometry pipelines. Our photometric method is able to restore subtle brightness variation that tends to be missed in conventional aperture photometric methods, and can be applied to other wide-field surveys suffering from PSF variations and trends. We are developing an analysis package for the next generation TAOS survey (TAOS II) based on the current experiments.