多阶导数自适应视频超分辨率重建

传统视频超分辨率重建算法在去除噪声的同时,很难有效保持图像边缘细节信息。针对该问题,构建了一种结合多阶导数数据项和自适应正则化项的视频超分辨率重建算法。在正则化重建模型的基础上,该算法对数据项进行改进,引入能更好描述噪声统计特性的噪声多阶导数,并利用去噪效果较好的全变分(TV)和非局部均值(NLM)正则化项对视频超分辨率重建过程进行约束。此外,为了更好地保持图像细节信息,采用区域空间自适应曲率差分算法提取结构信息,从而对正则化系数进行自适应加权。实验结果表明:在噪声方差为3时,与核回归算法和聚类算法相比,该算法重建视频主观效果边缘更加锐化,局部结构更加正确、清晰;重建视频的均方误差(MSE)平均下降幅度分别为25.75%和22.50%;峰值信噪比(PSNR)分别平均提升了1.35 dB和1.14 dB。所提算法能够在去除噪声的同时有效保持图像的细节特征。

Adaptive video super-resolution reconstruction algorithm based on multi-order derivative

The traditional video super-resolution reconstruction algorithm cannot preserve the details of the image edge effectively while removing the noise. In order to solve this problem, a video super-resolution reconstruction algorithm combining adaptive regularization term with multi-order derivative data item was put forward. Based on the regularization reconstruction model, the multi-order derivative of the noise, which described the statistical characteristics of the noise well, was introduced into the improved data item; meanwhile, Total Variation (TV) and Non-Local Mean (NLM) which has better denoising effect were used as the regularization items to constrain the reconstruction process. In addition, to preserve the details better, the coefficient of regularization was weighted adaptively according to the structural information, which was extracted by the regional spatially adaptive curvature difference algorithm. In the comparison experiments with the kernel-regression algorithm and the clustering algorithm when the noise variance is 3, the video reconstructed by the proposed algorithm has sharper edge, the structure is more accurate and clear; and the average Mean Squared Error (MSE) is decreased by 25.75% and 22.50% respectively; the Peak Signal-to-Noise Ratio (PSNR) is increased by 1.35 dB and 1.14 dB respectively. The results indicate that the proposed algorithm can effectively preserve the details of the image while removing the noise.