倍增比自适应的图像超分辨率重建

近年来，图像超分辨率重建一直是热门的研究领域，但是对于任意倍增比的超分辨率研究仍然鲜见相应的成果.在高倍增比的情形下，图像清晰度变得较低，肉眼已难以识别图像的内容.随着技术的进步，机器视觉已开始识别清晰度极低的图像，面向任意倍增比的超分辨率技术研究已显得日益重要.通过测试各种代表性的超分辨率算法，本文在进行图像超分辨率的任意倍增比分析之后，根据全尺度质量总和准则提出了一种倍增比自适应的超分辨率重建算法.实验结果表明，所提算法在整个倍增尺度内实现了更好的整体重建性能.

Scale-Adaptive Image Super-Resolution Reconstruction

In recent years, the image super-resolution reconstruction has always been a hot research field, but the corresponding research results about arbitrary-scale-ratio super resolution are still rare. Under high scale ratio, the image resolution will become lower, and it is difficult for human eyes to recognize such image content. With the advancement of technology, machine vision has been used to recognize the images with very low resolution, and the research on arbitrary-scale-ratio super resolution has become increasingly important. Through testing various representative super-resolution algorithms, this study proposes a scale-adaptive super-resolution reconstruction algorithm according to a full-scale quality sum criterion after performing extensive arbitrary-scale-ratio analysis on image super resolution. Experimental results show that the proposed algorithm can achieve better overall-reconstruction performance within the whole scale range.