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 ...

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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 processed under the low-resolution dictionary to obtain the low-resolution The sparse representation coefficient of the image, and finally the reconstructed high-resolution image can be obtained according to the sparse representation coefficient obtained by projection and the high-resolution dictionary

However, this method requires a large number of low-resolution images and high-resolution image blocks to ensure the adequacy of the prior contour detail information, the calculation is huge, and the image reconstruction time is long, resulting in low efficiency.

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