An image super-resolution reconstruction (SRR) algorithm based on the double parameters Beta process joint dictionary (BPJD) is proposed to improve the image SRR effect. Compared with those sparse representation SRR algorithms that are only applicable to single feature space, the BPJD learning dictionary is obtained in coupled feature spaces. Firstly, a training sample image is obtained according to the remote sensing image degradation model, and high and low resolution images are respectively patched and Gibbs sampled to generate the dictionary training samples. Then, a double-parameter joint dictionary is established to connect the high-low resolution image space according to the BPJD model. In the Beta process, sparse coefficients are expressed as the multiplication of coefficient weights and dictionary atoms and the joint dictionary mapping matrix is obtained by training and updating. Finally, remote sensing image super-resolution reconstruction is performed in sparse representation. Experiment results show that the proposed method can reduce the size of the dictionary adaptively, and can reconstruct the higher quality super-resolution image with the smaller sparse dictionary. The remote sensing image SRR result contains more texture details, and peak signal-to-noise ratio and structural SIMilarity are increased objectively.