基于分离字典的图像超分辨率重建

The algorithm of image super-resolution reconstruction via separable dictionaries

传统的基于稀疏表示的图像超分辨率重建算法,需要将图像进行分块并列化为向量,这样就破坏了图像块内邻域像素间的相关性.为了更好地利用图像邻域内的结构信息,本文结合分离字典能从不同方向对图像块进行稀疏表示的特性,提出了基于分离字典的图像超分辨率重建算法.实验结果表明,与传统基于稀疏表示的图像超分辨率重建算法相比,本文算法不仅提高了图像重建的速度,而且在PSNR和SSIM两个衡量指标上都优于传统基于稀疏表示的超分辨率重建算法(PSNR提高约0.2 dB, SSIM提高约0.01).

Traditional sparse representation-based super-resolution algorithms need to divide images into patches and then stack them into columns. This operation ignores the intrinsic 2 D structure and spatial correlation inherent in patches. In order to fully exploit 2 D spatial correlation in image patches, we combine the sparse representation ability of the separable dictionary in both the horizontal and vertical directions, and propose an algorithm for image super-resolution based on a separable dictionary. The experimental results show that our proposed algorithm not only improves the efficiency of image super-resolution, but also improves the PSNR and SSIM(i.e., about 0.2-dB PSRN better than traditional methods, and 0.01 SSIM better than existing methods).