基于边信息先验约束的压缩图像超分辨率重建技术

项目名称： 基于边信息先验约束的压缩图像超分辨率重建技术

项目编号： No.61471201

项目类型： 面上项目

立项/批准年度： 2015

项目学科： 无线电电子学、电信技术

项目作者： 干宗良

作者单位： 南京邮电大学

项目金额： 77万元

中文摘要： 图像超分辨率重建是图像处理中的重要研究课题。随着图像应用的不断涌现，超分辨率重建将有广泛的应用前景。在基于学习的图像超分辨率重建中，为了保证算法的普适性，要求训练样本与待处理图像统计无关。然而，在大数据时代，相似的图像数据有时能够顺利获取。本项目将压缩图像作为研究对象，主要关注于如何利用相似的图像数据作为边信息先验知识，进行压缩图像的超分辨率重建。主要内容包括：（1）压缩图像特征自适应描述和边信息图像片的相似性分析、建模；（2）利用边信息约束的压缩图像/视频盲超分辨率重建；（3）基于头肩像视频通信中，利用边信息约束的人脸超分辨率重建；（4）基于视觉显著性描述的自适应内容的超分辨率重建。本项目将搭建仿真平台，对上述研究内容进行测试和分析。本项目将为图像超分辨率逐步在我国走向实用提供理论指导和技术支持。

中文关键词： 超分辨率重建；边信息；自适应特征描述；人脸超分辨率重建；视觉显著性

英文摘要： Image super resolution(SR) is an important research topic in image processing, and this technology have broad application prospects with the emergence of image applications. In order to ensure that the SR algorithm can adapt to the various images, it generally assume that the training image date and test image data are statistically independent. However, the images, which are similar to current image, sometimes can be easy obtained, and these relevant image data can be defined as side information. In this reseach project, we mainly focus on the compressed image/video, and the main work is to systematically study the compressed image SR method by using the side information as the priori knowledge. The main research content in this project as follows:(1) adaptive feature description for compressed image and the similarity modeling between the current image data and side information data; (2)the blind SR reconstruction method for the compressed image through the side informaiton priori;(3) the face hallucination by using side information for face-to-face video communication;(4)the content adaptive SR method through saliency-based visual attention models. The above-mentioned methods will be simulated on PC platform, and the performance of the proposed algorithms also compared and analyzed. The research of the project will offer the theoretical foundation and technical support to the development of SR applications of our country.

英文关键词： Super resolution;Side information;Adaptive feature learning;Face hallucination;Visual saliency