Single-image super-resolution reconstruction method based on symmetric depth network

Abstract

The invention discloses a single-image super-resolution reconstruction method based on a symmetric depth network and belongs to the image processing technology field. The method mainly comprises the following steps of 1, making a high resolution image block and low resolution image block training set; 2, constructing a symmetric convolution-deconvolution depth network used for model training; 3, based on the constructed depth network and the made data set, carrying out network model training; and 4, based on a learned model parameter, inputting one low resolution image, wherein acquired output is a reconstructed high resolution image. In the invention, a convolution layer and a deconvolution layer are combined and simultaneously a network depth is increased; the network depth is used to increase network performance; a reconstruction capability of an image detail portion is enhanced and a good image super-resolution reconstruction effect is acquired. The method has a wide application prospect in fields of image high definition displaying, medical imaging, a remote sensing image and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and more specifically relates to a method for super-resolution reconstruction of a single image based on a symmetrical deep network. Background technique [0002] With the rapid development of high-definition display devices, people's demand for high-resolution images and videos is increasing. The traditional methods of acquiring high-resolution images are usually hardware-based methods, that is, improving the image sensor manufacturing process, including reducing the pixel size and increasing the sensor size. Due to the high cost of the hardware method and the inherent limitations of the hardware, other methods have to be sought. [0003] Image super-resolution reconstruction is the process of reconstructing one or more low-resolution images to obtain a high-resolution image. Compared with hardware methods, super-resolution reconstruction technology has a lower cost, and the reconstru...

AI Technical Summary

Problems solved by technology

[0010] In order to overcome the problem that the details of the reconstructed image in the above-mentioned prior art are easily lost, the present invention proposes a single image super-resolution reconstruction method based on a symmetrical deep network; the present invention combines the convolutional layer and the deconvolutional layer, At the same time, the network depth is increased, the network performance is improved by using the network depth, the reconstruction ability of the image details is strengthened, and a better image super-resolution reconstruction effect is obtained.

Images