VDSR超分辨率在真实图像和合成图像上的性能评价

D. Vint, G. Di Caterina, J. Soraghan, R. Lamb, D. Humphreys
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引用次数: 4

摘要

本文对甚深超分辨率(VDSR)架构的适用性进行了评估,以提高低质量图像的空间分辨率。为此,执行了两组测试。前者通过对真实生活图像的分析来确定网络对低分辨率图像的改善能力。第二个测试是在分辨率图表的图像上执行的,因此是合成的。这是为了分析网络的频率响应。对于每个测试,使用三个度量来评估图像质量。这些是峰值信噪比(PSNR),结构相似性指数(SSIM)和调制传递函数(MTF)。实验结果表明,VDSR网络在所有三个指标上都能够提高第一次测试的图像质量,从而表明该网络适用于超分辨率。第二个测试提供了更多信息,说明在给定高对比度图像时网络的局限性,以及由此产生的振铃效果。因此,与低分辨率图像相比,PSNR/SSIM值并没有得到改善,但MTF曲线更高,图像视觉更清晰。
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Evaluation of Performance of VDSR Super Resolution on Real and Synthetic Images
This paper presents an evaluation of the suitability of the Very Deep Super Resolution (VDSR) architecture, to increase the spatial resolution of lower quality images. For this aim, two sets of tests are performed. The former being on real life images to determine the networks ability to improve low resolution images. The second test is performed on images of a resolution chart, and therefore synthetic. This is to analyse the frequency response of the network. For each test, three metrics are used to assess image quality. These are the Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Modulation Transfer Function (MTF). Experimental results show that the VDSR network is able to increase the quality of the images within the first test in all three metrics, therefore showing that the network is suitable for super resolution. The second test provides more information on the limitations of the network when given a high contrast image, and the resulting ringing effects it can create. Therefore results in PSNR/SSIM values are not improved over the low resolution images, however they have a higher MTF curve as well as more visually sharp images.
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