Research On Super-Resolution Image Processing Algorithm Based On Interpolation And Worked Examples

People’s requirement for the exchange and processing of information becomes higher with the development of technology and society. Many images in the internet are low resolution (LR) images because of the limitation of the bandwidth and storage space. We can obtain high quality image through image super resolution processing technique such as interpolation and worked examples database etc. to satisfy the requirements of high resolution image or video. Of course, the application of super resolution image processing is not limited to the above aspects. Because image super-resolution reconstructed technology can handle image properly and improve the resolution of image which can hardly increase the cost and new equipment, super-resolution technology has wide development prospects.Image super resolution technology has already achieved favorable results in some methods, but the result of reconstruction has further research and improving space for the bad reconstructed effect of image details especially the edge part of image. This paper made a lot of investigations and proposed new super-resolution image processing algorithm based on interpolation and worked examples to improve the quality of reconstructed image.First of all, this thesis reviews and illustrates super resolution reconstruction technique and makes a classification of them, then doing a detail exposition and analysis of the research situation of each reconstruction method in each class. Advantages and disadvantages of super-resolution image reconstruction algorithm based on the bilinear interpolation and worked examples are illustrated in this thesis, which lays the theoretical foundation of the improved algorithm on the basis of above theory. The effect of ordinary bilinear interpolation is not satisfied, the thesis proposed bilinear interpolation reconstruction method based on edge detection which aims at the shortage of proposed interpolation method. It mainly uses Canny algorithm to detect edge of low resolution image, then binary the edge image and get information of edge position. There are ten kinds of different information of edges, so the pixel points need interpolated of the edge and around the edge are interpolated again by using different proportion on the pixels of edge and non edge after the location of the edge is determined. It is realized through MATLAB and evaluated through a combination of objective and subjective approach. The objective evaluation approach includes peal signal to noise ratio (PSNR) and feature similarity (SSIM). The experimental data shows that the improved method is better than the original interpolation method.Since worked samples method has blocking effect and the situation of one to many has shortcoming, this thesis proposed worked samples super resolution reconstruction algorithm based on increasing the adjacent edge information. The adjacent edge information of low resolution (LR) image block is added around the LR image. The low resolution image blocks include adjacent edge information and the corresponding high resolution (HR) image blocks are stored in the training base, which can improve the accuracy of matching. The new image can match more similar HR image block and achieve better constructed effect. Root mean square error (MSE) and mean feature similarity (MSSIM) are used to illustrate the better reconstruction effect of new proposed improved method.In conclusion, the two new improved algorithms provided in this article has certain innovation and can improve the constructing effect and quality of image super resolution to a certain extent. It provides new idea for further research of image super resolution reconstruction, it also has a strong theoretical and practical significance.