CAJ | 학술논문

最近，双字典训练已成为在计算机视觉和图像领域解决超分辨率复原问题的有力工具。针对基于双字典训练的图像超分辨率算法中字典训练与重构阶段的重构误差，提出了一种基于自适应多字典对的超分辨<br>　　率复原算法。通过对样本进行聚类并训练多特征字典来适应不同类型的输入图像。在字典训练阶段，充分利用了不同次训练字典产生的差异，在重建中筛选高频补丁，进行多次重构，有效地提升了重构图像的质量。实验仿真与比较表明，该方法在重构图像的质量上有所提高，且能提供更清晰的细节。
최근，쌍자전훈련이성위재계산궤시각화도상영역해결초분변솔복원문제적유력공구。침대기우쌍자전훈련적도상초분변솔산법중자전훈련여중구계단적중구오차，제출료일충기우자괄응다자전대적초분변<br>　　솔복원산법。통과대양본진행취류병훈련다특정자전래괄응불동류형적수입도상。재자전훈련계단，충분이용료불동차훈련자전산생적차이，재중건중사선고빈보정，진행다차중구，유효지제승료중구도상적질량。실험방진여비교표명，해방법재중구도상적질량상유소제고，차능제공경청석적세절。
Recently, double dictionary training has emerged as a powerful tool for solving a class of super-resolution reconstruction problems in computer vision and image processing. To reduce the reconstruction error of dictionary training and reconstruction within image super-resolution reconstruction algorithm via double dictionary training, this paper proposes a super-resolution reconstruction algorithm based on adaptive multiple dictionary pairs (AMDP). This algorithm is available for different types of input images by samples clustering and training features dictionary. In dictionary training, differences among different dictionaries training are used to filter high frequency patches and reconstruct images repetitiously in restoration, which effectively improves the quality of reconstructed images. The experiments show that the proposed algorithm achieves improvement in image quality and provides more details.