Single Image Super-resolution Reconstruction Method And Its Application In Visual Displacement Measurement

With the development of times and technological progress,people's demand for information is constantly increasing,especially for the visual sense information.As one of the important carriers for people to transmit information,people's demand for the resolution of image is also increasing day by day with the rapid development of image processing technology.High-resolution image can provide more detailed information of the target object,which plays an important role in image analysis and processing.However,in practical applications,due to the physical limitations of the imaging equipment,such as the limitations of optical devices,processor performance,storage capacity,as well as the interference effect of the actual environment,the images obtained by people have certain degradation.How to enhance the acquired image through existing equipment conditions,improve the spatial resolution of the image,and meet people's sensory needs of the image has become the focus of image application research-image super-resolution technology research can be divided into time-domain super-resolution technology and spatial domain super-resolution technology.In view of the spatial domain super-resolution technology which commonly used,it is to point to reconstructe higher spatial resolution image from one picture or a series of low resolution images,so that it can be divided into single image super-resolution reconstruction technique and sequence images super-resolution reconstruction technology.In the super-resolution reconstruction of a single image,sparse representation,as a new image representation model,has been widely used in the field of image processing for its role in depicting the internal structure and essential attributes of the image,and can achieve better performance.The image reconstruction technology based on regularization also has a remarkable effect in the field of super-resolution reconstruction of sequential images.However,in the final analysis,the image super-resolution reconstruction technology aim to enhance image details as much as possible based on keeping characteristics of the original image.This paper studied the scale invariability of image characteristics in gaussian difference space,through a combination of Laplacian pyramid image restoration technology and feature enhancement technique to realize the simple and effective image super-resolution reconstruction process,which can greatly enhance the image quality and the effect of visual sense.What's more,it can be used combined with sparse expression and regularization techniques to further improve the effect of image super-resolution reconstruction.The main contents of this paper are as follows:(1)The research status of image super-resolution reconstruction technology is discussed in this paper,the theoretical basis of image super-resolution restoration is elaborated,and the basic concepts of sparse representation theory and regularization algorithm,mathematical model and application in the field of image processing are explained in detail.(2)In view of the image edge details enhancement research,this paper proposes a single image super-resolution reconstruction techniques based on gaussian difference space and Laplacian pyramid image restoration.This method takes the gaussian difference in space has the scale invariance of image features as the image enhancement material,then replace Laplacian pyramid decomposition image in the process of Laplacian pyramid image restoration.Due to the scale invariance of the gaussian difference space,it can make the reconstruction image does not change the basic characteristics of the original image and strengthen image edge details in the realization process of image super-resolution reconstruction.At the same time,further de-noising processing can be proceed in the feature space to achieve a certain de-noising effect,which can further enhance the original image.In this paper,sparse expression algorithm and regularization algorithm are combined to enhance the super-resolution reconstruction of the original single image and sequence images.Compared with the original algorithm,the enhanced images have better visual perception quality.(3)Aim at the application effect of super-resolution image applied in the vision measurement,this paper proposes a new image displacement metering scheme,which can directly calculate the promotion effect measuring results of the reconstruction image and the original low resolution image,and can be directly express the image displacement measurement accuracy level of image super-resolution reconstruction.Based on this measurement scheme,this paper studied the performance of various image super-resolution reconstruction algorithms in image displacement measurement.The results show that the image super-resolution reconstruction technology has a certain application prospect in the field of image measurement.