The invention discloses an improved self-adaptive multi-dictionary learning image super-resolution reconstruction method, which comprises the steps of: (1) determining a downsampling matrix D and a fuzzy matrix B according to a quality degradation process of an image; (2) establishing a pyramid by utilizing the self-similarity of the image, regarding an upper-layer image and a natural image of the pyramid as samples of dictionary learning, constructing various types of dictionaries Phi k by adopting a PCA method, and regarding a top-layer image of the pyramid as an initial reconstructed image X<^>; (3) calculating a weight matrix A of nonlocal structural self-similarity of sparse coding; (4) setting an iteration termination error e, a maximum number iteration times Max_Iter, a constant eta controlling nonlocal regularization term contribution amount and a condition P for updating parameters; (5) updating current estimation of the image; (6) updating a sparse representation coefficient; (7) updating current estimation of the image; (8) updating a self-adaptive sparse domain of X if mod(k, P)=0, and using X<^><k+1> for updating the matrix A; (9) and repeating the steps from (5) to (8), and terminating iteration until the iteration meets a condition shown in the description or k>=Max_Iter.