To improve the robustness of the data singular points of super-resolution (SR) algorithm

we propose an SR algorithm based on K-means clustering and support vector data description (Kmeans-SVDD). The proposed algorithm is composed of two stages: training and testing. In the training stage

the approximating sub-band of trained images is clustered by the K-means method.SVDD is employed to drop singular points for each cluster. Finally

principle component analysis (PCA) is used to train the approximation and the detail sub-band dictionaries in the wavelet domain. In the testing stage

the approximating sub-band of the input low-resolution (LR) image is used as the approximating sub-band of the high-resolution (HR) image. This step is conducted on the basis of the observation that the approximating sub-band of the HR image is similar to it fo the corresponding LR image. Then

the prepared dictionaries are utilized to recover the detail sub-bands of the HR image. Finally

converse wavelet transformation is used to obtain the recovered HR image. The proposed Kmeans-SVDD algorithm is shown to be superior to existing algorithms with an average PSNR improvement of 1.82 dB

0.37 dB

0.30 dB

and 0.15 dB over the bicubic interpolation

Zeyde's algorithm

ANR algorithm

and K-means-PCA algorithm

respectively. Extensive tests indicate that once the SVDD process is added before the dictionary training

the outliers can be removed and the quality of the dictionary can be finally improved. By reconstructing each band separately in the wavelet domain

the influences caused by the uncertain high-frequency information in low-frequency image to image super-resolution can be prevented; thus

reliable detail information can be restored.