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

Abstract

The invention discloses a multi-level recursive feature fusion image super-resolution reconstruction system and a method thereof, and relates to the technical field of image restoration. This system is: 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 sequence, and the initial feature extraction Module (10) interacts with multi-level feature fusion module (30) and adder module (40) respectively. This method is: ① initial feature extraction; ② recursive feature extraction; ③ multi-level feature fusion; ④ deep feature generation; ⑤ high-resolution image generation. The present invention can obtain high-quality super-resolution reconstructed images while greatly reducing the implementation complexity of the system and reducing the calculation time; it is suitable for applications such as video monitoring and medical imaging.

Description

technical field [0001] The present invention relates to the technical field of image restoration, in particular to a multi-level recursive feature fusion image super-resolution reconstruction system and its method; system and its method. Background technique [0002] Single-frame image super-resolution aims to reconstruct the original high-resolution image from the observed single-frame low-resolution image, which has a wide range of applications in medical imaging, digital photography, and video surveillance. Traditional single-frame image super-resolution methods can be interpolation-based methods, model-based methods, and learning-based methods. In recent years, with the successful application of deep learning theory in the field of image classification and recognition, research on image super-resolution reconstruction methods based on deep convolutional neural networks has also received extensive attention. [See literature: [1] Dong C, Loy C C, He K, et al. Image super...

AI Technical Summary

Problems solved by technology

However, in the past super-resolution reconstruction based on deep recursive convolutional neural network, the reconstruction module usually only used the extracted deep feature information, while ignoring the low-level features for the final reconstruction, which greatly affected the reconstruction. Image Quality

Images