In order to ensure the high speed of image display and storage in real-time, the image captured by the popular driving recorder usually shows a low resolution, which has a serious impact on effective image information acquisition under unexpected situation. To solve this problem, a perspective transformation based on self-examples of the images and high-frequency compensation were used to reconstruct the city street images with low resolution. Perspective transformation was added to the affine transformation to match image patches, match image patch and high frequency compensation was used to recover the lost high frequency information of each matched image patch when image pyramid was constructed. The image pyramid was searched by non-local multi-scale method to get the matched patches, which were synthesized to obtain the images of high resolution. Many low resolution street view images were used to verify the effectiveness of this algorithm. Compared it to existing typical algorithms such as ScSR (Sparse coding Super-Resolution), Upscaling, SCN (Sparse Coding based Network), the experimental results show that the algorithm in several blind evaluation indices is better than other algorithms and it can improve the image resolution while keeping the edges and details of the image.