In this talk we present and algorithm for signal reconstruction from absolute value of frame coefficients. Then we compare its performance to the Cramer-Rao Lower Bound (CRLB) at high signal-to-noise ratio. To fix notations, assume {f_i; 1<= I <= m} is a spanning set (hence frame) in R^n. Given noisy measurements d_i=||^2+\nu_i, 1<= i<= m, the problem is to recover x\in R^n up to a global sign. In this talk the reconstruction algorithm solves a regularized least squares criterion of the form