The invention relates to an
infrared image super-resolution reestablishing method based on the
compressed sensing theory. According to the method, low-resolution images are used as a foundation and are subjected to partitioning, low-resolution image blocks are regarded as downsampling observation corresponding to high-resolution image blocks, a downsampling model is established, and a downsampling matrix is written. A sparse
transformation matrix of the high-resolution image blocks is established and is multiplied by an
observation matrix, and a sensing matrix is obtained. According to the low-resolution blocks and the sensing matrix, an OMP
algorithm is used for reestablishing sparse coefficients of the high-resolution image blocks, then the sparse
transformation matrix is multiplied by the sparse coefficients, and the high-resolution image blocks are obtained. Finally, all the high-resolution image blocks are spliced, and a high-resolution reestablished image is obtained. The method has the advantages of being easy to achieve, quick in operation, stable in performance and good in anti-
noise effect, difference operation is used for generating a difference
transformation matrix and achieving sparse transformation, complex computing of redundant dictionary training is avoided,
shot noise of images can be well removed, and the
noise reduction
advantage is achieved.