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腾讯的ARC实验室近期推出了一项名为Real-ESRGAN的开源深度学习模型,该模型专为提升低分辨率图像的质量而设计,能够在没有真实高分辨率图像参照的情况下,通过合成的退化过程进行训练,实现了盲超分辨率技术。

提升图像质量与去除伪影

Real-ESRGAN的主要功能在于将低分辨率图像转换为高分辨率,同时保持或增强图像细节和纹理,减少模糊和噪点。在图像放大过程中,它能够有效地识别并减少常见的图像伪影,如振铃和过冲,从而提供更清晰、更自然的高分辨率图像。

模拟真实世界图像退化

通过使用高阶退化模型,Real-ESRGAN能够模拟多种现实世界中可能出现的图像退化情况,包括相机模糊、传感器噪声、锐化处理和JPEG压缩等。这种技术使得模型在处理实际场景的图像时,能够更加接近真实效果。

无需真实高分辨率图像的训练

与传统的超分辨率方法不同,Real-ESRGAN的训练过程不依赖于真实高分辨率图像。它通过合成的退化数据生成训练集,这一创新方法降低了对高分辨率图像资源的依赖,扩大了模型的适用范围。

增强图像细节

在提升分辨率的同时,Real-ESRGAN特别注重增强图像的局部细节,如纹理、边缘和轮廓,使得放大后的图像更加鲜明,视觉效果更佳。

官方资源与工作原理

Real-ESRGAN的工作原理基于深度学习和生成对抗网络(GAN)。首先,模型通过模拟多种退化过程合成训练数据。接着,它使用一个类似ESRGAN的生成器网络,结合多个残差密集块(RRDBs),从低分辨率图像恢复高分辨率图像。训练过程中,首先使用L1损失函数训练一个以峰值信噪比(PSNR)为导向的模型(Real-ESRNet),然后结合L1损失、感知损失和GAN损失进行优化,以生成更高质量的高分辨率图像。

Real-ESRGAN的发布,为图像处理领域提供了一个强大的新工具,将有助于提升各种应用场景中的图像质量,包括摄影、影视制作、医疗成像以及许多其他领域。

【source】https://ai-bot.cn/real-esrgan/

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在 “腾讯发布Real-ESRGAN:开源图像无损放大,重塑高清视觉体验” 有 1 条评论
  1. This post is worth saving and periodically rereading so as not to forget important truths

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