The invention discloses an image super-resolution
reconstruction method based on multitask KSVD (K
singular value decomposition)
dictionary learning, mainly aims at solving the problem that the quality of a reconstructed image of the existing method is relatively reduced seriously under a high-
magnification factor. The method comprises the following steps of: inputting a training image, filteringthe image to extract characteristics; extracting tectonic characteristics vector sets of small characteristic blocks, and clustering to obtain sample pair sets {(H1, L1), (H2, L2), ..., (HK, LK)} of K to
high resolution and
low resolution; developing K high-resolution dictionaries Dh1, Dh2, ..., DhK and corresponding low-resolution dictionaries Dl1, Dl2, ..., DlK from the K groups of sample pair sets by means of a KSVD method; encoding low-resolution patterns input in the low-resolution dictionaries Dl1, Dl2, ..., DlK; obtaining an initial reconstruction image by encoding and high-resolution dictionaries Dh1, Dh2, ..., Dh; then implementing local constrained optimization of the initial reconstruction image; and compensating residual errors and implementing
global optimization treatment toobtain a final reconstruction image. The image super-resolution
reconstruction method based on multitask KSVD
dictionary learning has the advantages that the various natural images can be reconstructed, the quality of the reconstructed image can be effectively improved under the condition of a high-
magnification factor, and the method can be applied to the recover and identification of human, animal,
plant and building and other target objects.