Real Time Super Resolution Using Non-Linear Signal Processing
Real Time Super Resolution Using Non-Linear Signal Processing
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DOI：
10.23919/wac.2018.8430426
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发表时间：
2018-06
期刊：
影响因子：
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通讯作者：
S. Gohshi
中科院分区：
文献类型：
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作者：
S. Gohshi
超分辨率图像重建(SRR)是一种典型的超分辨率图像重建技术，其研究成果多种多样。SRR算法最初是针对静止图像提出的。它使用许多低分辨率图像来重建高分辨率图像。不幸的是，在实践中，我们很少有足够数量的低分辨率图像来支持SRR。通常，只有一个(或几个)模糊图像。另一方面，在从安全和照片恢复到缩放功能以及与印刷业相关的无数其他例子的应用中，需要改进模糊图像。最近，SRR被扩展到具有许多相似帧的视频序列，这些帧可以用作低分辨率图像来重建高分辨率帧。在正常的SRR中，人们从一幅高分辨率图像中采样的低分辨率图像重建一幅高分辨率图像，但在视频应用中，低分辨率视频帧并不是从高分辨率视频帧中获取的。本文提出了一种新的分辨率改进方法，无需如此高分辨率的图像即可工作。其算法简单，可应用于单图像和实时视频系统。
Super resolution image reconstruction (SRR) is a typical super resolution (SR) technology that has been researched with varying results. The SRR algorithm was initially proposed for still images. It uses many low-resolution images to reconstruct a high-resolution image. Unfortunately, in practice, we rarely have a sufficient number of low-resolution images for SRR to work. Usually, there is only one (or a few) blurry images. On the other hand, there is a need to improve blurry images in applications ranging from security and photo restoration to zooming functions and countless other examples related to the printing industry. Recently, SRR was extended to video sequences that have many similar frames that can be used as low-resolution images to reconstruct high-resolution frames. In normal SRR, one reconstructs a high-resolution image from low-resolution images sampled from one high-resolution image, but in the video application, the low-resolution video frames are not taken from higher resolution ones. This paper proposes a novel resolution improvement method that works without such a high-resolution image. Its algorithm is simple and can be applied to a single image and real-time video systems.