An Improved Image Super-Resolution Reconstruction Method Based On LapSRN

Lei Kong, L. Jiao, Feng Jia, Kai Sun
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引用次数: 0

Abstract

With the gradual maturity of the traditional static image recognition field, super-resolution reconstruction based on deep neural networks is a research hotspot and difficulty in the field of computer vision. In particular, most single-frame image super-resolution methods have problems such as loss of high-frequency information, noise introduced in the up-sampling process, and difficulty in determining the interdependence between each channel of the feature map when reconstructing the predicted image. In order to solve the above problems, we introduce back projection mechanism into the LapSRN network in this paper. By introducing the back projection mechanism effectively improved the consistency between the extracted image feature data and the target feature data feature, and thereby improved the reconstructed image parameters. Experiments show that the improved network can achieve better performance than LapSRN.
一种改进的基于LapSRN的图像超分辨率重建方法
随着传统静态图像识别领域的逐渐成熟,基于深度神经网络的超分辨率重建是计算机视觉领域的研究热点和难点。特别是,大多数单帧图像超分辨率方法存在高频信息丢失、上采样过程中引入噪声以及在重建预测图像时难以确定特征映射各通道之间的相互依赖性等问题。为了解决上述问题,本文在LapSRN网络中引入了反向投影机制。通过引入反向投影机制,有效地提高了提取的图像特征数据与目标特征数据特征的一致性,从而提高了重建图像的参数。实验表明,改进后的网络比LapSRN具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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