Abstract
With advances in artificial intelligence, image processing has gained significant interest. Image super-resolution is a vital technology closely related to real-world applications, as it enhances the quality of existing images. Since enhancing fine details is crucial for the super-resolution task, pixels that contribute to high-frequency information should be emphasized. This paper proposes two methods to enhance high-frequency details in super-resolution images: a Laplacian pyramid-based detail loss and a repeated upscaling and downscaling process. Total loss with our detail loss guides a model by separately generating and controlling super-resolution and detail images. This approach allows the model to focus more effectively on high-frequency components, resulting in improved super-resolution images. Additionally, repeated upscaling and downscaling amplify the effectiveness of the detail loss by extracting diverse information from multiple low-resolution features. We conduct two types of experiments. First, we design a CNN-based model incorporating our methods. This model achieves state-of-the-art results, surpassing all currently available CNN-based and even some attention-based models. Second, we apply our methods to existing attention-based models on a small scale. In all our experiments, attention-based models adding our detail loss show improvements compared to the originals. These results demonstrate our approaches effectively enhance super-resolution images across different model structures.
Abstract (translated)
随着人工智能的发展，图像处理领域引起了越来越多的关注。超分辨率技术作为其中的重要分支，在提升现有图像质量方面发挥了关键作用。由于增强细节对于超分辨率任务至关重要，因此需要强调那些有助于提供高频信息的像素。本文提出两种方法来增强超分辨率图像中的高频细节：一种是基于拉普拉斯金字塔的细节损失（Laplacian pyramid-based detail loss），另一种则是重复放缩过程（repeated upscaling and downscaling process）。总损失函数结合了我们的细节损失，通过分别生成和控制超分辨率图像和细节图像来引导模型。这种方法使模型能够更有效地聚焦于高频成分，从而提高超分辨率图像的质量。此外，重复放大缩小过程则可以通过从多个低分辨率特征中提取多样信息来增强细节损失的效果。 我们进行了两类实验。首先，设计了一种基于卷积神经网络（CNN）的模型，并将其与我们的方法相结合。该模型取得了最先进的成果，在现有的所有 CNN 基础上甚至某些注意力机制基础的模型性能之上。其次，我们将这些方法小规模地应用到现有的注意力机制基础上的模型中。在我们所有的实验中，加入我们细节损失后的基于注意力机制的模型相比于原始版本均有所改进。这些结果表明我们的方法能够在不同的模型结构下有效提升超分辨率图像的质量。
URL
https://arxiv.org/abs/2601.09410
