CAJ | 학술논문

　　基于学习的超分辨率方法通常根据低分辨率图像从样本库中选取若干特征相似的匹配对象，再使用优化算法进行超分辨率估计，但其结果受匹配对象的质量限制，并且匹配特征一般只选择图像的几何结构信息，匹配准确性较低。提出了稀疏字典编码的超分辨率模型，将高、低分辨率图像特征块统一进行稀疏编码，建立高、低分辨率图像的稀疏关联，同步实现匹配搜索和优化估计，突破了上述方法的限制。应用形态分量分析法提取图像的特征数据，提高了特征匹配的准确性，并同步实现超分辨率重建和降噪功能。优化方法采用稀疏K-SVD算法以提高稀疏字典编码的计算速度。采用自然图像进行实验，与其他基于学习的超分辨率算法相比，重建所得到的图像质量更优。
　　기우학습적초분변솔방법통상근거저분변솔도상종양본고중선취약간특정상사적필배대상，재사용우화산법진행초분변솔고계，단기결과수필배대상적질량한제，병차필배특정일반지선택도상적궤하결구신식，필배준학성교저。제출료희소자전편마적초분변솔모형，장고、저분변솔도상특정괴통일진행희소편마，건립고、저분변솔도상적희소관련，동보실현필배수색화우화고계，돌파료상술방법적한제。응용형태분량분석법제취도상적특정수거，제고료특정필배적준학성，병동보실현초분변솔중건화강조공능。우화방법채용희소K-SVD산법이제고희소자전편마적계산속도。채용자연도상진행실험，여기타기우학습적초분변솔산법상비，중건소득도적도상질량경우。
Learning-Based super-resolution methods usually select several objects with similar features from some examples according to the low-resolution image, then estimate super-resolution result using optimization algorithm. But the result is usually limited by the quality of matching objects and only geometric construction of the images is selected as matching feature, so matching accuracy is relatively low. This paper presents a sparse dictionary model for image super-resolution, which unifies the feature patches of high-resolution (HR) and low-resolution (LR) images for sparse coding. To break through the aforementioned limitations, this method builds a sparse association between HR and LR images, and realized simultaneous matching and optimization methods. The study uses a MCA method to improve the accuracy for feature extraction and carry out super-resolution reconstruction and denoise simultaneously. Sparse K-SVD algorithm is adopted as optimization method to reduce the computation time of sparse coding. Some experiments with real images show that this method outperforms other learning-based super-resolution algorithms.