An infrared small target detection method is proposed based on sparse representation assisted by ridge regression. The proposed method constructs over-complete dictionary with target samples and background samples which are produced by two-dimensional Gaussian model and normal random matrix respectively. Infrared small target detection consists of two stages. In the first stage, the construction errors of detect samples are calculated fast using ridge regression. In the second stage, candidate samples are selected adaptively by ridge regression reconstruction errors, and infrared small targets are detected with the reconstruction errors of the candidate samples selected which are computed with sparse representation. The experimental results on several infrared images show that the proposed method is faster and more robust than the existing methods.