论文标题
基于图像超分辨率的跨尺度过程相似性插值
Across-scale Process Similarity based Interpolation for Image Super-Resolution
论文作者
论文摘要
图像超分辨率技术的关键步骤是插值，旨在生成高分辨率图像，而无需引入诸如模糊和振铃之类的伪影。在本文中，我们提出了一种通过输注通过利用“过程相似性”计算的高频信号组件进行插值的技术。通过“过程相似性”，我们指的是图像分解以分辨率与图像分解以另一种分辨率的分解之间的相似之处。在我们的方法中，通过离散小波（DWT）和固定小波（SWT）变换获得了生成图像细节和近似值的分解。利用DWT和SWT的互补性质，以获得输入图像与其低分辨率近似之间的结构关系。结构关系由通过粒子群优化（PSO）获得的最佳模型参数表示。由于过程相似性，这些参数用于从输入图像生成高分辨率输出图像。将所提出的方法与六种现有技术在定性，SSIM和FSIM测量方面以及计算时间（CPU时间）进行了比较。发现我们的方法在CPU时间方面是最快的，并产生可比的结果。
A pivotal step in image super-resolution techniques is interpolation, which aims at generating high resolution images without introducing artifacts such as blurring and ringing. In this paper, we propose a technique that performs interpolation through an infusion of high frequency signal components computed by exploiting `process similarity'. By `process similarity', we refer to the resemblance between a decomposition of the image at a resolution to the decomposition of the image at another resolution. In our approach, the decompositions generating image details and approximations are obtained through the discrete wavelet (DWT) and stationary wavelet (SWT) transforms. The complementary nature of DWT and SWT is leveraged to get the structural relation between the input image and its low resolution approximation. The structural relation is represented by optimal model parameters obtained through particle swarm optimization (PSO). Owing to process similarity, these parameters are used to generate the high resolution output image from the input image. The proposed approach is compared with six existing techniques qualitatively and in terms of PSNR, SSIM, and FSIM measures, along with computation time (CPU time). It is found that our approach is the fastest in terms of CPU time and produces comparable results.
