Among the dictionary-based Super-Resolution (SR) algorithms, the Anchored Neighborhood Regression (ANR) algorithm has been attracted widely attention due to its superior reconstruction speed and quality. However, the anchored neighborhood projections of ANR are unstable to cover varieties of mapping relationships. Aiming at the problem, an image SR algorithm based on adaptive anchored neighborhood regression was proposed, which adaptively calculated the neighborhood center based on the distribution of samples in order to pre-estimate the projection matrix based on more accurate neighborhood. Firstly, K-means clustering algorithm was used to cluster the training samples into different clusters with the image patches as centers. Then, the dictionary atoms were replaced with the cluster centers to calculate the corresponding neighborhoods. Finally, the neighborhoods were applied to pre-compute the projection matrix from LR space to HR space. Experimental results show that the average reconstruction performance of the proposed algorithm on Set14 is better than that of other state-of-the-art dictionary-based algorithms with 31.56 dB of Peak Signal-to-Noise Ratio (PSNR) and 0.8712 of Structural SIMilarity index (SSIM), and even is superior to the Super-Resolution Convolutional Neural Network (SRCNN) algorithm. At the same time, in terms of the subjective performance, the proposed algorithm produces sharp edges in reconstruction results with little artifacts.

Combining Discrete Wavelet Transform (DWT), Stationary Wavelet Transform (SWT) and Non-Local Means (NLM), a new single-frame Super-Resolution (SR) method named DSNLM was proposed to eliminate the blurring effect in wavelet domain SR image. In DSNLM, the subbands were obtained by applying DWT to low-resolution input image, and SWT was simultaneously applied to obtain high frequency subbands; Then NLM filter was applied to these composite subbands along with the interpolated input image. Finally, Inverse Discrete Wavelet Transform (IDWT) was applied to these subbands to obtain the SR image. The experimental and visual results verify the superiority of the proposed method over the conventional image resolution enhancement techniques with improved Peak Signal-to-Noise Ratio (PSNR), Mean Squared Error (MSE) and Structural SIMilarity (SSIM), and it is effective in denoising and blurring.