An Example-based Method in Multi-frame Super Resolution
In this paper, a two-stage super resolution method that combines the multi-frame and example-based methods is proposed. Traditionally, the multi-frame super resolution method only utilizes the continuity prior and complementary information among the low-resolution(LR) images with sub-pixel misalignment. While the example-based method digs the prior from abundant training images, but performs low ability to process the severe blurring image. So in our paper, firstly, the sequence is processed by the traditional fast and robust super resolution method to enhance the definition. Then, in the second stage, the high-resolution feature (HRF)/high-resolution(HR) dictionary pairs is prepared. The near-high-resolution image acquired in the former stage is split into overlapped patches, then sparse coded to the HRF dictionary, and linear combined with the HR dictionary atoms. The experiments on the synthetic and real image sequence prove that the proposed method outstands from the other methods.
Yu Zhu Yanning Zhang Haisen Li
Shaanxi Key Laboratory of Speech and Image Information ProcessingSchool of Computer Science, Northwe Shaanxi Key Laboratory of Speech and Image Information Processing School of Computer Science, Northw
国际会议
2011亚太信号与信息处理协会年度峰会(APSIPAASC 2011)
西安
英文
1-6
2011-10-18（万方平台首次上网日期，不代表论文的发表时间）