Terahertz image super-resolution reconstruction method, system and related device

Abstract

The terahertz image super-resolution reconstruction method provided by the invention comprises the following steps: preprocessing acquired images to obtain training set images, and inputting the training set images into a generation network; carrying out convolution processing and image style processing on the training set image by utilizing a generation network to generate a sample image; whereinthe network structure in the generation network is an RRDB network structure; performing model training by using the sample image and a standard image corresponding to the training set image to obtain an image super-resolution reconstruction model; and inputting the preset terahertz image into the image super-resolution reconstruction model to obtain an output result. According to the method, thenetwork structure in the generated network is the RRDB network structure, so that the terahertz image feature extraction capability is improved, and the super-resolution of the terahertz image can beimproved. The invention further provides a terahertz image super-resolution reconstruction system and device and a computer readable storage medium which all have the above beneficial effects.

Description

technical field [0001] The present application relates to the technical field of terahertz image super-resolution reconstruction, and in particular to a method, system, device and computer-readable storage medium for terahertz image super-resolution reconstruction. Background technique [0002] With the continuous advancement of information technology and the advent of the digital age, images, as one of the main information media, have been widely used in various scenarios. In many fields, people have certain requirements for image quality. Affected by limiting factors such as hardware devices, the images generally obtained by people are of low resolution, which is difficult to meet the needs of specific scenarios in the rapidly developing information age. An important indicator for measuring image quality is the spatial super-resolution of the image. If the resolution is higher, it will contain more image details and information accuracy, which is very beneficial for subse...

AI Technical Summary

Problems solved by technology

However, the SRGAN algorithm is mainly aimed at visible light images and not passive terahertz images. The imaging mechanism is different. The network structure in the generation network of the SRGAN algorithm is a 16-layer residual network structure. For images reconstructed from terahertz passive images, image shaping Poor, low resolution, difficult to meet subsequent identification and other engineering needs

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