基于自适应锚定邻域回归的图像超分辨率算法

在基于字典的图像超分辨率(SR)算法中,锚定邻域回归超分辨率(ANR)算法由于其优越的重建速度和质量引起了人们的广泛关注。然而,ANR算法的锚定邻域投影并不稳定,以致于不足以涵盖各种样式的映射关系。因此提出一种基于自适应锚定邻域回归的图像SR算法,根据样本分布自适应地计算邻域中心从而以更精确的邻域来预计算投影矩阵。首先,以图像块为中心,运用K均值聚类算法将训练样本聚类成不同的簇;然后,用每个簇的聚类中心替换字典原子来计算相应的邻域;最后,运用这些邻域来预计算从低分辨率(LR)空间到高分辨率(HR)空间的映射矩阵。实验结果表明,所提算法在Set14上平均重建效果以31.56dB的峰值信噪比(PSNR)及0.8712的结构相似性(SSIM)优于其他基于字典的先进算法,甚至胜过超分辨率卷积神经网络(SRCNN)算法。同时,在主观表现上看,所提算法恢复出了尖锐的图像边缘且产生的伪影较少。

Image super-resolution algorithm based on adaptive anchored neighborhood regression

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.