基于自相似性车载采集城市街景图像的重建

大众化的车载为确保实时、高速的图像显示及图像存储，其捕获的图像通常会呈现出较低的分辨率，严重影响了突发状况时有效图像信息的获取。针对该低分辨率的城市街景图像采用了一种基于透视变换、高频补偿的自相似性图像重建方法。该算法在仿射变换的基础上增加了透视变换来进行图像块的匹配，并对每一个匹配的图像块进行高频补偿以恢复构建图像金字塔时丢失的高频信息，通过多尺度非局部方法搜索图像金字塔，合成匹配图像块得到最终的高分辨率图像。采用该算法对采集到的大量低分辨率城市街景图像进行重建，并与ScSR、Upscaling、SCN这三种典型的算法进行对比，实验结果表明该算法在几种盲评价指标上较其他算法好，在提高图像分辨率的同时能保持图像的边缘和细节信息。

Self-examples reconstruction of city street image from driving recorder

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.