A multi-channel fast super-resolution image reconstruction algorithm based on matrix observation model is proposed in the paper, which consists of three steps to avoid the computational complexity; a single image SR reconstruction step, a registration step and a wavelet-based image fusion. This algorithm decomposes two large matrixes to the tensor product of two little matrixes and uses the natural isomorphism between matrix space and vector space to transform cost function based on matrix-vector products model to matrix form. Furthermore, we prove that the regularization part can be transformed to the matrix formed. The conjugate-gradient method is used to solve this new model. Finally, the wavelet fusion is used to integrate all the registered high-resolution images obtained from the single image SR reconstruction step. The proposed algorithm reduces the storage requirement and the calculating complexity, and can be applied to large-dimension low-resolution images.