Application of Regularization Technique and Low-Rank Matrix in Super-Resolution Algorithm via Sparse Representation

To effectively utilize the image features as priori knowledge for guiding reconstruction and solve the problem that conventional super-resolution algorithms suffer from insufficient recovery of details such as edges and structures,an improved super-resolution algorithm is proposed.The image to be reconstructed was decomposed into a low-rank sub-image and a sparse sub-image with different features by the low-rank matrix recovery.The low-rank sub-image was reconstructed with the proposed super-resolution algorithm based on sparse representation and regularization technique.Firstly,the non-local similarity regularization term was constructed by using similar image patches found in the low-rank sub-image to obtain the non-local redundancy of the image to preserve the edge information.Then the manifold learning regularization term was constructed by applying the locally linear embedding method to get prior knowledge of the image structure to enhance the structural information.The sparse sub-image was not involved in super-resolution algorithm based on sparse representation,and instead it was reconstructed with bi-cubic interpolation.Experimental results demonstrate that the proposed algorithm has significant improvement over other algorithms in terms of subjective visual effects,peak signal-to-noise ratio,and structural similarity measure.