Image super-resolution reconstruction method based on deep learning

Abstract

The invention discloses an image super-resolution reconstruction method based on deep learning, and aims to train low-resolution data by using deep learning technology in order to obtain a mapping function from low resolution to high resolution. The method comprises steps of: (1) downsampling a data set; (2) adding the convolutional activation results of different layers by using a residual principle; (3) classifying training data into a labeled class and a unlabeled class which correspond to two loss numbers respectively; and (4) integrating the two classes to obtain a final loss function. Any one low-resolution image is input into a trained neural network model such that the output of the neural network is a reconstructed super-resolution image. The method can effectively improve the quality of the obtained image without changing the hardware of an imaging system.

Description

technical field [0001] The invention belongs to the field of image processing, in particular to an image super-resolution reconstruction method based on deep learning. Background technique [0002] Image super resolution (super resolution, SR) is the process of obtaining a high-resolution image from a low-resolution image. This technology is mainly used to enhance the spatial resolution of the image, which can break through the limitations of the original system imaging hardware conditions. Re-acquired high-resolution images have the characteristics of higher resolution, more detailed information, and higher quality picture quality, and are currently one of the most effective and lowest-cost ways to obtain high-precision images. [0003] In the process of image acquisition, limited by factors such as imaging conditions and imaging methods, the imaging system usually cannot obtain all the information in the original scene, and will be affected by many factors such as deformat...

AI Technical Summary

Problems solved by technology

[0006] (1) SR is an inverse problem. For a low-resolution image, there may be many different high-resolution images corresponding to it

[0007] (2) The low-quality images obtained in real multimedia applications are often complex degraded images with multiple degraded factors, such as low resolution, out-of-focus blur, motion blur, compression distortion and noise, etc.

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