Gaussian mixture model-based contrast enhancement

In this study, a method for enhancing low-contrast images is proposed. This method, called Gaussian mixture model-based contrast enhancement (GMMCE), brings into play the Gaussian mixture modelling of histograms to model the content of the images. On the basis of the fact that each homogeneous area in natural images has a Gaussian-shaped histogram, it decomposes the narrow histogram of low-contrast images into a set of scaled and shifted Gaussians. The individual histograms are then stretched by increasing their variance parameters, and are diffused on the entire histogram by scattering their mean parameters, to build a broad version of the histogram. The number of Gaussians as well as their parameters are optimised to set up a Gaussian mixture modelling with lowest approximation error and highest similarity to the original histogram. Compared with the existing histogram-based methods, the experimental results show that the quality of GMMCE enhanced pictures are mostly consistent and outperform other benchmark methods. Additionally, the computational complexity analysis shows that GMMCE is a low-complexity method

An optimal method for scheduling observations of large sky error regions for finding optical counterparts to transients

The discovery and subsequent study of optical counterparts to transient sources is crucial for their complete astrophysical understanding. Various gamma ray burst (GRB) detectors, and more notably the ground--based gravitational wave detectors, typically have large uncertainties in the sky positions of detected sources. Searching these large sky regions spanning hundreds of square degrees is a formidable challenge for most ground--based optical telescopes, which can usually image less than tens of square degrees of the sky in a single night. We present algorithms for optimal scheduling of such follow--up observations in order to maximize the probability of imaging the optical counterpart, based on the all--sky probability distribution of the source position. We incorporate realistic observing constraints like the diurnal cycle, telescope pointing limitations, available observing time, and the rising/setting of the target at the observatory location. We use simulations to demonstrate that our proposed algorithms outperform the default greedy observing schedule used by many observatories. Our algorithms are applicable for follow--up of other transient sources with large positional uncertainties, like Fermi--detected GRBs, and can easily be adapted for scheduling radio or space--based X--ray followup.Comment: Submitted to ApJ. 18 pages, 15 figure