A Residual Instance Regression Super-Resolution Reconstruction Method Based on Multi-level Dictionary Learning

Abstract

The invention discloses a residual instance regression super-resolution reconstruction method based on multi-level dictionary learning. For the feature vector of the image block, use K-SVD to learn a dictionary with strong representation ability as the anchor point; use the learned dictionary to perform least squares regression on the low-resolution and high-resolution blocks in the block to obtain a linear mapping relationship; Estimate the high-resolution features, calculate the reconstruction error, and map the estimated high-resolution features with the reconstruction error while doing further dictionary learning; after the L layer, a set of residual regressions is obtained; use the input image and the obtained The residual regressor performs reconstruction, and the obtained high-resolution features are used for the reconstruction of the next layer; all estimated high-resolution image blocks are summed and calculated to synthesize a high-resolution image. The invention has stronger super-resolution capability and can be used for enlarging low-resolution natural images.

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 is similar to that based on k – Compared with the super-resolution algorithm of manifold learning, NN has achieved better results in reconstruction quality and computational complexity, but for each input low-resolution image block, this type of algorithm needs to be in the learning stage and The reconstruction phase solves the sparse representation of the over-complete dictionary

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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