A New Super-Resolution Algorithm for a Single Image Based on Local Structure Similarity

Single-image zooming is an ill-posed problem due to constrained information which may be provided. This paper pays attention to the similarity feature among local structures in an image which can be maintained across scale. Based on the feature, we propose a new method using similarity. By the method, first, we try to find out all the similar structure sets under certain similarity criterion and obtain the degree of similarity. Then according to the degree sorted, image sequences in similarity are generated. As a result, we can apply known algorithms in the field of image sequence super-solution to solve the problem. This paper selects the MAP method and computes the optimal resolution by the steep-descending iterations. Several experiments are presented to demonstrate the effectiveness of the approach, especially in the area of IC, where the images are often with plenty of similar structures.