Robust Algorithm for Large-Scale Gaussian Patterns Localization

Efficient accurate Gaussian localization is an important topic in many applications, e.g. localization based super-resolution microscopy and image scanning microscopy, which requires large-scale Gaussian patterns localization for accurate super-resolution image reconstruction. Existing Gaussian localization methods usually require high signal-to-noise image and the existing standard fitting algorithm usually requires manually inputting a good initial value for all parameters, which could be not convenient to use and difficult to guarantee high robustness for large-scale Gaussian localizations with a computer. It would be even more challenge to detect all the Gaussian patterns with high-dynamic-range of amplitudes, as well as to estimate a good initial value for all parameters for efficient Gaussian fitting and guarantee high robustness of the localization algorithm for low signal-to-noise ratio image data with strong background. In this paper, we propose an efficient Gaussian patterns detection technique and a robust Gaussian fitting method for accurate Gaussian fitting without initial estimation. In our technique, a fast Pearson correlation algorithm is proposed to improve the efficiency of the calculation of normalized cross correlation for large scale object detection with template matching. By introducing blind background estimation, a modified iterative least-squares Gaussian fitting algorithm without initials estimation is proposed for robust Gaussian fitting with noisy data with strong background. The simulation shows that the performance of the proposed detection technique is high for low SNR image and an efficiency improvement of 27% can be achieved; the proposed Gaussian fitting algorithm is capable of calculating all parameters without initial estimation, and the resulting fitting accuracy is very close to exiting standard methods, which indicates that image signal-to-noise ratio higher than 10dB is required to obtain subpixel accuracy.