An image super-resolution reconstruction method

Abstract

The invention discloses an image super-resolution reconstruction method. The method comprises the following steps: making a learning sample set; Learning and training: a single-frame model of the convolutional neural network is adopted as a recurrent network module, a super-resolution mapping model of a recurrent neural network mode comprising N + 1 recurrent network modules is constructed, and inthe training process, a single lowest-resolution image is input into each recurrent network module in the super-resolution mapping model for single-time training; Replacing a low-resolution picture and repeatedly carrying out the single training; Carrying out single training for multiple times; And reconstruction: inputting the low-resolution image to be reconstructed into the trained super-resolution mapping model to obtain a high-resolution image. According to the method, the model is guided to learn low-frequency and high-frequency information of the image at the same time by adding the output error items related to the blurred image in the performance indexes, so that the image super-resolution reconstruction result can give consideration to reliability and details; And meanwhile, thestorage space can be greatly saved through the super-resolution mapping model in a cyclic network form.

Description

technical field

[0001] The invention belongs to the technical field of software and relates to image processing technology, in particular to an image super-resolution reconstruction method. Background technique

[0002] Image super-resolution reconstruction technology is a means of generating high-resolution images from low-resolution images, and has great application value in medical images, satellite photography, security monitoring and other fields. Super-resolution reconstruction techniques can be divided into three categories: interpolation-based methods, model-based methods, and learning-based methods. Among them, the learning-based method is the current mainstream direction, especially with the continuous development of deep learning technology and convolutional neural network, the image quality of super-resolution reconstruction is also improving. It learns image features and a low-resolution to high-resolution mapping model through a large number of pairs of low-re...

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