Super-resolution image reconstruction system based on self-adaptation submodel dictionary choice

Abstract

The invention provides a super- resolution image reconstruction system based on a self-adaptation submodel dictionary choice. The super-resolution image reconstruction system based on the self-adaptation submodel dictionary choice comprises an input module, a high and low frequency training set construction module, a candidate base vector gathering and building module, a submodel dictionary choice module, a test image preprocessing module, a super-resolution image reconstruction module and an output module, wherein the high and low frequency training set construction module comprises a band allocation submodel and a primitive block extraction submodel; the candidate base vector gathering and organizing module comprises an online dictionary learning submodel and a DCT dictionary construction submodel; the test image preprocessing module comprises a low frequency smoothing submodel and a primitive block extraction submodel. The super-resolution image reconstruction system based on the self-adaptation submodel dictionary choosing is applied to different dictionary sizes and decimation factors, the super-resolution image reconstruction system based on the self-adaptation submodel dictionary choosing can significantly improve the subjective and objective quality of a reconstitution image, the high effective dictionary design process is guaranteed, and a novel visual angle is provided for the existing image compression standard at the same time.

Description

Technical field [0001] The invention relates to a scheme in the technical field of image reconstruction, in particular to a super-resolution image reconstruction system based on adaptive sub-model dictionary selection. Background technique [0002] In a large number of digital image applications, people often expect higher resolution images. Image resolution indicates the level of detail of image information, while high-resolution images mean higher pixel density, finer image quality, and more detailed information. Because the super-resolution image reconstruction technology is not limited by the constraints of sensor manufacturing technology, but obtains higher-resolution images through image processing algorithms, it has been widely used in many fields such as satellite meteorology, medical imaging, and image compression. However, there are still many problems that need to be solved in this technical field. For example, the traditional interpolation-based method will cause the...

AI Technical Summary

Problems solved by technology

However, since the approximate K-SVD method does not ensure asymptotic convergence to the optimal solution, the reconstructed images are of low quality for dictionaries trained from image sequences with different statistical properties

Images