Residual instance regression super-resolution reconstruction method based on multistage dictionary learning

Abstract

The invention discloses a residual instance regression super-resolution reconstruction method based on multistage dictionary learning, and the method comprises the following steps: generating a training set through high-resolution images, and establishing block pairs of low-resolution and high-resolution image blocks; extracting feature vectors of low-resolution image blocks, and learning a dictionary with strong representation ability by using K-SVD as an anchor point; performing the least square regression of low-resolution and high-resolution blocks in the block pairs through the dictionaryobtained via learning, and obtaining a linear mapping relation; estimating the high-resolution features, calculating a reconstruction error, and carrying out the mapping of the estimated high-resolution features and the reconstruction error while the further dictionary learning of the estimated high-resolution features; obtaining a group of residual regression devices after the L layer; carryingout the reconstruction through an inputted image and the obtained residual regression devices, and enabling the obtained high resolution features to be used for the reconstruction of a next layer; adding all estimated high-resolution image blocks and forming a high-resolution image through synthesis. The method is stronger in super-resolution capability, and can be used for the amplification of alow-resolution natural image.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a residual instance regression super-resolution reconstruction method based on multi-level dictionary learning. Background technique [0002] In practical applications, the imaging system is limited by many factors such as device cost, transmission bandwidth, computing resources, and imaging environment. The resolution of the obtained images is often not high, which brings great challenges to subsequent image processing, analysis, and understanding tasks. How to obtain high-resolution digital images is a topic of great concern to people. Undoubtedly, improving the physical resolution of the imaging system is the most direct and effective means to obtain high-resolution images. However, this method is limited by manufacturing technology and device cost, and is limited to some special applications, which is not easy to promote in practical applications; moreover, for many...

AI Technical Summary

Problems solved by technology

Although this type of algorithm has achieved better results in reconstruction quality and computational complexity compared with super-resolution algorithms based on k-NN and manifold learning, for each input low-resolution image block, This type of algorithm needs to solve the sparse representation of the over-complete dictionary in the learning phase and the reconstruction phase

Therefore, when the size of the dictionary or the image to be reconstructed is large, the computational cost of the algorithm is still very high, and it is difficult to be widely used

The method based on instance regression directly maps the relationship between low-resolution and high-resolution features. Although this type of method can ensure the quality of reconstruction and improve the effectiveness of reconstruction, the method based on instance regression is difficult to map low-resolution and high-resolution features. When there is a nonlinear relationship between images, a simple feature linear mapping is used, which is difficult to express the complex geometric structure information between low-resolution and high-resolution images

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