Image super-resolution reconstruction system based on multi-stage recursive feature fusion and method thereof

A technology of super-resolution reconstruction and feature fusion, which is applied in the field of image restoration, can solve problems affecting the quality of reconstructed images, ignore them, etc., and achieve the effect of reducing implementation complexity, reducing network parameters, and improving representation capabilities

Inactive Publication Date: 2019-07-05
SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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  • Abstract
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  • Application Information

AI Technical Summary

Benefits of technology

This patented technology uses multiple layers of processing that are combined together to create an improved method called Deep Learning (DL). It achieves this by performing complex operations on both data from different sources like pixels or audio signals while also utilizing advanced techniques such as regression analysis. By combining these results into one layer, DL helps improve accuracy without making any assumptions about how well each component works overall.

Problems solved by technology

The technical problem addressed by this patented method is how to efficiently recover images that have lost detail or noise due to factors like resolution loss caused by small details captured during scanning (such as blurriness) without compromising its overall effectiveness over traditional techniques such as denoising autoencoders.

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  • Image super-resolution reconstruction system based on multi-stage recursive feature fusion and method thereof
  • Image super-resolution reconstruction system based on multi-stage recursive feature fusion and method thereof
  • Image super-resolution reconstruction system based on multi-stage recursive feature fusion and method thereof

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Embodiment Construction

[0049] Below in conjunction with accompanying drawing and embodiment describe in detail:

[0050] 1. System

[0051] 1. Overall

[0052] Such as figure 1 , the system includes an initial feature extraction module 10, a recursive feature extraction module 20, a multi-level feature fusion module 30, an adder module 40 and an upsampling reconstruction module 50;

[0053] The initial feature extraction module 10, the recursive feature extraction module 20, the multi-level feature fusion module 30, the adder module 40 and the upsampling reconstruction module 50 interact in turn, and the initial feature extraction module 10 interacts with the multi-level feature fusion module 30 and the adder module respectively. 40 interactions.

[0054] In detail: the initial feature extraction module 10 has an input terminal and two output terminals, the recursive feature extraction module 20 has two input terminals and N output terminals, and the multi-level feature fusion module 30 has (N+2) in

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Abstract

The invention discloses an image super-resolution reconstruction system based on multi-stage recursive feature fusion and method thereof, and relates to the technical field of image restoration. The system is characterized in that an initial feature extraction module (10), a recursive feature extraction module (20), a multi-stage feature fusion module (30), an adder module (40) and an up-samplingreconstruction module (50) are interacted in sequence, and the initial feature extraction module (10) is interacted with the multi-stage feature fusion module (30) and the adder module (40). The method comprises the following steps: 1, extracting initial features; 2, extracting recursive feature; 3, fusing multistage feature; 4, generating depth features; 5, generating a high-resolution image. According to the method, while the high-quality super-resolution reconstructed image is obtained, the realization complexity of the system is well reduced, and the calculation time is shortened. The method is suitable for video monitoring, medical imaging and other applications.

Description

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Claims

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Application Information

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Owner SOUTH CENTRAL UNIVERSITY FOR NATIONALITIES
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