基于梯度格矩阵描述的图像神经风格迁移

Heng Jin, Tian Wang, Mengyi Zhang, Mingmin Li, Yan Wang, H. Snoussi
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引用次数: 2

摘要

尽管神经风格迁移在风格化图片上表现优异,但我们发现Gatys等[1]算法不能完美地重建纹理风格。输出的程式化图像会出现局部浑浊、纹理表达不足等不满意的意外纹理。该方法在原有算法的基础上,增加了对风格损失的梯度图描述,旨在增强纹理表达,消除浑浊。我们的方法在一定程度上延长了运行时间,但其输出的风格化图片在纹理细节上得到了更高的性能,特别是在消除模糊方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Neural Style Transfer for Picture with Gradient Gram Matrix Description
Despite the high performance of neural style transfer on stylized pictures, we found that Gatys et al [1] algorithm cannot perfectly reconstruct texture style. Output stylized picture could emerge unsatisfied unexpected textures such like muddiness in local area and insufficient grain expression. Our method bases on original algorithm, adding the Gradient Gram description on style loss, aiming to strengthen texture expression and eliminate muddiness. To some extent our method lengthens the runtime, however, its output stylized pictures get higher performance on texture details, especially in the elimination of muddiness.
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