预测偏移量的超分辨率:用于栅格化图像的超高效超分辨率网络

Jinjin Gu, Haoming Cai, Chenyu Dong, Ruofan Zhang, Yulun Zhang, Wenming Yang, Chun Yuan
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引用次数: 3

摘要

渲染高分辨率(HR)图形带来了大量的计算成本。高效的图形超分辨率(SR)方法可以用较小的计算资源实现HR渲染,引起了工业界和研究界的广泛研究兴趣。我们提出了一种新的计算机图形学实时SR方法,即预测偏移的超分辨率(SRPO)。我们的算法将图像分为两部分进行处理,即尖锐边缘和平坦区域。对于边缘,与以往以抗混叠图像为输入的SR方法不同,我们提出的SRPO利用栅格化图像的特性对栅格化图像进行SR。为了补充HR和低分辨率(LR)栅格化图像之间的残差,我们训练了一个超高效的网络来预测偏移映射,将适当的周围像素移动到新的位置。对于平坦区域,我们发现简单的插值方法已经可以产生合理的输出。最后通过引导融合运算,将网络生成的尖锐边缘与插值方法得到的平坦区域进行融合,得到最终的SR图像。该网络仅包含8434个参数,并可通过网络量化加速。大量的实验表明,与现有的最先进的方法相比,所提出的SRPO可以以更小的计算成本获得更好的视觉效果。
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Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images
Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution by Predicting Offsets (SRPO). Our algorithm divides the image into two parts for processing, i.e., sharp edges and flatter areas. For edges, different from the previous SR methods that take the anti-aliased images as inputs, our proposed SRPO takes advantage of the characteristics of rasterized images to conduct SR on the rasterized images. To complement the residual between HR and low-resolution (LR) rasterized images, we train an ultra-efficient network to predict the offset maps to move the appropriate surrounding pixels to the new positions. For flat areas, we found simple interpolation methods can already generate reasonable output. We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image. The proposed network only contains 8,434 parameters and can be accelerated by network quantization. Extensive experiments show that the proposed SRPO can achieve superior visual effects at a smaller computational cost than the existing state-of-the-art methods.
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