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局部纹理图案估计图像细节超分辨率

Fan Fan, Yang Zhao, Yuan Chen

IEEE transactions on pattern analysis and machine intelligence

|March 3, 2025

概括

本研究介绍了一种新的图像超分辨率 (SR) 网络,可以恢复高频纹理,而无需生成对抗网络 (GAN). 新的局部纹理模式估计 (LTPE) 方法增强了纹理细节的重建.

科学领域:

计算机视觉计算机视觉
图像处理图像处理

背景情况:

深度超分辨率 (SR) 网络正在努力恢复随机高频纹理.
生成对抗网络 (GAN) 在纹理恢复方面表现出色,但存在诸如大参数和伪造纹理的可能性等缺点.

研究的目的:

提出一种新的SR网络,用于恢复精细的高频纹理细节,而不依赖GAN.
解决现有的SR方法在准确重建复杂纹理方面的局限性.

主要方法:

开发了一个基于局部纹理模式估计 (LTPE) 的新型SR网络.
设计了一个可微分的局部纹理操作员来提取纹理结构.
实现了一个纹理增强分支,用于预测高分辨率的本地纹理分布.
使用了由估计的纹理地图指导的纹理融合SR分支.
使用L1损失和Gram损失优化了网络.

主要成果:

拟议的基于LTPE的SR网络有效地恢复高频纹理.
该方法可以在没有GAN结构的情况下实现高质量的纹理重建.
恢复的高频细节受到局部纹理分布的限制,减少了生成错误.

结论:

基于LTPE的SR网络为实现现实的纹理恢复提供了GAN的可行替代方案.
这种方法提高了图像SR中高频纹理恢复的准确性和真实性.

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