CAJ | 학술논문

提出一种利用广义非局部均值和自相似性的图像超分辨率算法。该算法不仅利用图像的自相似性将低分辨率图像与其下采样图像作为一个训练库，而且利用非局部平均算法的良好特性提高复原图像的质量。该算法首先提取低分辨率图像的高斯差特征系数，然后利用广义非局部平均算法来估计待复原图像丢失的高频细节，获得高分辨率图像。实验结果表明，该算法对图像取得较好的复原效果，复原出的高分辨率图像更接近于真实图像，与其他方法相比，具有更好的主观和客观质量。
제출일충이용엄의비국부균치화자상사성적도상초분변솔산법。해산법불부이용도상적자상사성장저분변솔도상여기하채양도상작위일개훈련고，이차이용비국부평균산법적량호특성제고복원도상적질량。해산법수선제취저분변솔도상적고사차특정계수，연후이용엄의비국부평균산법래고계대복원도상주실적고빈세절，획득고분변솔도상。실험결과표명，해산법대도상취득교호적복원효과，복원출적고분변솔도상경접근우진실도상，여기타방법상비，구유경호적주관화객관질량。
A super-resolution method based on generalized nonlocal mean and self-similarity is proposed. The proposed method not only adopts the self-similarity of the image by taking the low-resolution image and its downsampled version as a training set but uses the nonlocal mean algorithm to improve the quality of the restored image.The proposed method first extracts the features of the low image by using the difference of Gaussians,and then a generalized nonlocal mean algorithm is adopted to estimate the high-frequency details of the low image.Experimental results show that the proposed algorithm has a good performance,and that the high-resolution image generated by the proposed method is of better subj ective and obj ective quality compared with other methods.