To solve the problems of slow convergence speed, low search precision and poor robustness in traditional double chains quantum genetic algorithm, a new double chains quantum genetic algorithm (F _DCQGA) is proposed. The coding space is mapped to reduce the algorithm searching space and increases searching density, under the premise of guaranteeing quantum population adaptation and argument population monotonicity. The adaptive step-length factor is introduced to the quantum updating, which changes the step-length with gradient of objective function in searching points. This could solve the global optimal solution search difficulties caused by oscillatory occurrence in traditional optimization algorithm. Quantum π/6 gate is presented in chromosome mutation upadating, to overcome the shortcoming that NOT gate can not update quantum bit probability amplitude. The F_DCQGA is applied to the threshold selection of wavelet threshold denoising. Simulation results show that F_DCQGA improves the convergence speed of the wavelet threshold function and searching precision. And in image edge feature extraction, the smaller mean square error (S_ME) and larger peak signal to noise ratio (R_PSN) are gained. Simultaneously, the high frequency information is also retained.