在红外超分辨率成像应用中，提高主观视觉效果有着很现实的需求.当前基于深度学习的图像超分辨率重建方法大多以客观评价指标为损失函数进行训练和优化，主观评价方法因量化困难而难以应用，为此本文着重研究了主观评价和各种可量化的客观评价指标的相关性，发现相位一致性特征与主观评价结果关联度高.据此设计了基于主客观联合评价的损失函数，应用于红外图像超分辨率重建算法，实验表明，在保持客观质量评价的同时，更好地提高了图像的主观视觉效果.

In the application of infrared super-resolution imaging, it is actually necessary to improve the subjective visual effects of images. At present, most image super-resolution reconstruction methods based on deep learning are trained and optimized with objective evaluation index as loss function. The subjective evaluation methods are difficult to apply due to the difficulty of quantification. Therefore, this paper focuses on the correlation between subjective evaluation and quantifiable objective evaluation indexes, and finds that the characteristics of phase consistency are highly correlated with subjective evaluation results. Based on this, a loss function based on subjective and objective joint evaluation is designed and applied to the super-resolution reconstruction algorithm of infrared image. Experiments show that this method can improve the subjective visual effect of image while maintaining the objective evaluation score.