Image super-resolution reconstruction method based on double-dictionary learning

Abstract

The invention discloses an image super-resolution reconstruction method based on double-dictionary learning, which mainly solves the problem that detailed information cannot be effectively supplemented in the prior art when super-resolution reconstruction is performed on a low-resolution image. A realization process comprises the following steps of: firstly, inputting a low-resolution image XL to be processed, constructing five pairs of high-resolution dictionaries and low-resolution dictionaries (Dh1, Dl1), (Dh2, Dl2),..., (Dh5, Dl5), and reconstructing five high-resolution estimation images under the five pairs of dictionaries; constructing one pair of high-frequency dictionary and low-resolution dictionary Df={Dhf, Dlf} by virtue of the high-frequency information and low-frequency information of the input low-resolution image, and reconstructing five pairs of high-resolution estimation images with different neighbor parameters; and finally, performing low-rank decomposition on the ten pairs of reconstructed high-resolution estimation images, and solving a mean value of a low-rank matrix obtained from the decomposition to obtain a final reconstructed high-resolution image XH. The method provided by the invention can be used for obtaining the high-resolution image with clear edges and rich details when being used for performing the super-resolution reconstruction on the low-resolution image and is suitable for super-resolution reconstruction on various natural images.

Description

technical field [0001] The invention belongs to the technical field of image processing, in particular to a method for super-resolution reconstruction of low-resolution images, which can be used for super-resolution reconstruction of various natural images. Background technique [0002] Image super-resolution reconstruction refers to using one or more low-resolution images to reconstruct a clear high-resolution image according to the corresponding algorithm, which is an important and challenging research content in image processing. It is widely used in video surveillance and high-definition television imaging. At present, a lot of research work has been done on image super-resolution reconstruction at home and abroad, and many classic algorithms have been proposed. [0003] The more traditional image super-resolution reconstruction methods include bilinear interpolation, bicubic interpolation, iterative back projection, convex set projection and so on. These methods have ...

AI Technical Summary

Problems solved by technology

However, the high-resolution image obtained by this algorithm lacks detailed information and the edges of the image are blurred; since then, Yang et al. have proposed an algorithm based on sparse representation dictionary learning. For details, see the document "Super-Resolution Via Sparse Representation" IEEE Trans.Image Process, 2010, vol.19, pp: 2861-2872, the algorithm first obtains the low-resolution dictionary and the high-resolution dictionary through the method of dictionary learning, and then projects the low-resolution image to be proces...

Images