Super-Interpolation With Edge-Orientation-Based Mapping Kernels for Low Complex 2× Upscaling.
Super-Interpolation With Edge-Orientation-Based Mapping Kernels for Low Complex 2× Upscaling.
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DOI：
10.1109/tip.2015.2507402
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Jae-Seok Choi;Munchurl Kim
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文献类型：
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作者：
Jae-Seok Choi;Munchurl Kim
随着超高清（UHD）视频服务的出现，通常需要超分辨率（SR）技术来从诸如HD图像的低分辨率（LR）图像生成高分辨率（HR）图像。为了在具有硬件和软件的有限计算设备中生成这样的HR图像和UHD分辨率的视频，特别需要低复杂度但优异的SR方法。在本文中，我们提出了一种新的和快速的SR方法，称为超插值（SI），通过统一的插值步骤和质量增强步骤。所提出的SI方法利用基于边缘方向（EO）的预学习内核，它继承了简单的插值和SR的质量增强。它执行SR直接从输入图像的初始分辨率到放大输出图像的目标分辨率，而不需要任何中间插值图像。所提出的SI方法包括离线训练和在线放大阶段。在离线训练阶段，训练LR图像块基于它们的边缘方向被聚类成不同的EO类，对于这些EO类，在训练LR和HR图像块之间学习类相关线性映射函数。在放大阶段，通过应用基于LR输入图像块的EO选择的适当线性映射函数来生成每个LR输入图像块的HR输出图像块。我们提出的SI方法进行了深入的比较，与十个国家的最先进的SR方法，常见的图像集和许多HD/UHD图像。实验结果表明，SI方法产生最小的运行时间，并需要相对较小的硬件资源。该方法在平均（峰值信噪比）PSNR/（结构相似性）SSIM方面优于现有的六种方法，并且与其他方法相比，其PSNR/SSIM性能具有竞争力或稍低。
With the advent of ultrahigh-definition (UHD) video services, super-resolution (SR) techniques are often required to generate high-resolution (HR) images from low-resolution (LR) images, such as HD images. To generate such HR images and a video of UHD resolutions in limited computing devices with hardware and software, low complex but excellent SR methods are particularly required. In this paper, we present a novel and fast SR method, called super-interpolation (SI), by unifying an interpolation step and a quality-enhancement step. The proposed SI method utilizes edge-orientation (EO)-based pre-learned kernels, which inherits the simplicity of interpolation and the quality enhancement of SR. It performs SR directly from the initial resolution of an input image to the target resolution of an up-scaled output image without requiring any intermediate interpolated image. The proposed SI method involves offline training and online up-scaling phases. In the offline training phase, training LR image patches are clustered based on their edge orientations into different EO classes for which class-dependent linear mapping functions are learned between training LR and HR image patches. In up-scaling phase, an HR output image patch for each LR input image patch is generated by applying an appropriate linear mapping function selected based on the EO of LR input image patch. Our proposed SI method is intensively compared with the ten state-of-the-art SR methods for common image sets and many HD/UHD images. The experimental results show that the SI method yields the smallest running time and requires relatively small hardware resources. It outperforms the six state-of-the-art methods in average (peak signal-to-noise ratio) PSNR/(structural similarity) SSIM, and exhibits competitive or somewhat lower PSNR/SSIM performance compared with the others.