CAJ | 학술논문

针对单幅低分辨率图像的超分辨率重建问题，提出了一种基于自训练字典学习的超分辨率重建算法。首先根据图像的退化模型，对输入的低分辨率图像进行降质处理，然后利用 K-SVD 方法训练字典，获得重建所需要的先验知识，最后根据先验知识重建高分辨率图像。仿真实验的结果表明，利用该方法获得的高分辨率图像在视觉效果和客观评价上均优于传统方法，同时算法的时间效率也有很大的提升。
침대단폭저분변솔도상적초분변솔중건문제，제출료일충기우자훈련자전학습적초분변솔중건산법。수선근거도상적퇴화모형，대수입적저분변솔도상진행강질처리，연후이용 K-SVD 방법훈련자전，획득중건소수요적선험지식，최후근거선험지식중건고분변솔도상。방진실험적결과표명，이용해방법획득적고분변솔도상재시각효과화객관평개상균우우전통방법，동시산법적시간효솔야유흔대적제승。
Based on the self-learning dictionary, a super-resolution reconstruction method of single image is proposed. First of all, according to the image degradation model, the low-resolution image input is processed with blurred and downsampled operations. Then the dictionary is trained with K-SVD method, and we obtain the priori knowledge for reconstruction. Finally, the high-resolution image is reconstructed based on the priori knowledge. The result of simulation experiment shows that the method is superior to conventional methods in the visual effects and objective evaluation, and the time efficiency of the algorithm is also significantly improved.