Stochastic resonance is different from the traditional signal processing methods such as the Wiener filtering, which uses the noise to process the weak signal in the nonlinear system under the strong noise. Considering advantages and disadvantages of the stochastic resonance and the Wiener filtering, the weak signal processing self-adaptation restoration algorithm based on Stochastic Resonance and Wiener Filtering is put forward and implemented in the paper. The dimension of image signals is reduced by the line scanning method and the column scanning method, and the image signals’ quality is improved by stretching transform. Then the processing output is optimized by the Wiener filtering. The simulation results and practical application show that the proposed algorithm has good robustness, and the algorithm has better performance than the traditional restoration algorithms such as the Wiener filtering and wavelet transform. The algorithm has better effect on the noise filtering and image contrast and resolution enhancing, especially in the recovery of the signal with strong noise pollution, the result of this algorithm to filter noise and image restoration becomes better. The algorithm overcomes the problem of bad processing effect under high SNR signal and poor robustness, and has a certain application prospect in weak signal extraction, especially in strong noise and dark environment.