Super-resolution image reconstruction method using analysis sparse representation

The invention relates to a super-resolution image reconstruction method based on analysis sparse representation, belonging to the technical field of image processing. The method comprises the following steps of: performing dictionary training according to a training sample set; and training a high-resolution dictionary and a low-resolution dictionary for an extracted feature; converting an image to be input from an RGB (Red, Green and Blue) space into a 1 alpha beta space and dividing into blocks of a same size; performing two kinds of operation on the blocks, wherein one is that each block is amplified by using the conventional amplification method and the other one is that an residual image of each block is extracted, sparse representation of the residual image in the low-resolution dictionary is calculated, and then the residual image is reconstructed in the high-resolution dictionary to obtain a reconstructed residual image; summarizing results of the two steps, converting back into the RGB space and performing back projection to obtain the reconstructed super-resolution image. According to the method, the image reconstruction noise can be obviously reduced, and detail features are kept; and meanwhile, the method has the advantages of easiness in operation and wide application.