常见的图像情感转换方法基于迁移图像颜色可以迁移图像情感的假设，但由于图像内容的影响，直接迁移图像颜色并不能完全迁移图像情感，而且需要先获得合适的参考图像，再进行图像颜色迁移，但在实际应用中，会面临情感上与目标图像情感相近、内容上与源图像相近的参考图像获取困难、颜色迁移时需考虑局部对象语义一致性等问题。为此，文中提出了一种基于自适应亮度调节的图像情感转换方法。该方法根据心理学中图像亮度与图像情感（又称愉悦度，简称V值）具有显著相关性，通过深度神经网络ISTNet自适应地调节亮度，将图像转换到目标图像情感。首先，从现有的图像情感数据集中获取一幅图像及其对应的真实V值，通过改变图像亮度，可获得一系列亮度不同的图像；然后，通过预训练图像V值回归器预测这些内容相同而亮度不同的图像对应的伪V值；最后，利用这些图像和伪V值训练ISTNet，以学习图像亮度调节和情感变化之间的内在联系。在实际应用时，无需任何参考图像，直接将待转换图像和目标V值输入神经网络ISTNet，就可以得到对应情感标签的输出图像。实验结果表明，该方法的图像情感转换性能优于现有的基于颜色的图像情感迁移方法。

Common image sentiment transformation methods are based on the assumption that transferring image color can transfer image sentiment. However, due to the influence of image content, transferring image color cannot completely transfer image sentiment, and it is necessary to obtain a suitable reference image before transferring image color. However, in practical application, there will be difficulties in obtaining reference images that are similar to the target image in sentiments and similar to the source image in content, and the semantic consistency of local objects need to be considered when transferring image color. Therefore, this paper proposed an image sentiment transformation method based on adaptive brightness adjustment. According to the significant correlation between image brightness and image sentiment (also known as Valence value, abbreviated as V value) in psychology, the method adaptively adjusts brightness through deep neural network ISTNet to convert the image to target image sentiment. First, an image and its corresponding true V value were obtained from the existing image emotion dataset. By changing the image brightness, a series of images with different brightness can be obtained. Then, the pseudo V values corresponding to the images with the same content but different brightness were predicted by the pre-trained image V value regression. Finally, ISTNet was trained with these images and pseudo V values to learn the internal relationship between image brightness adjustment and sentimental change. In practical application, without any reference image, directly input the image and the target V value into the neural network ISTNet to obtain the output image of the corresponding sentimental tag. The experimental results show that the performance of this method is better than the existing color based image sentiment transformation methods.