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.

翻译：随着人工智能的进步，图像处理领域获得了广泛关注。图像超分辨率作为一项与现实应用密切相关的关键技术，能够有效提升现有图像的质量。由于增强精细细节对于超分辨率任务至关重要，因此需要重点处理贡献高频信息的像素。本文提出了两种增强超分辨率图像高频细节的方法：基于拉普拉斯金字塔的细节损失和重复上下采样过程。结合我们细节损失的总体损失函数通过分别生成并控制超分辨率图像和细节图像来指导模型训练。该方法使模型能更有效地聚焦于高频分量，从而获得质量更优的超分辨率图像。此外，重复上下采样过程通过从多个低分辨率特征中提取多样化信息，进一步放大了细节损失的有效性。我们进行了两类实验验证：首先，我们设计了融合所提方法的基于CNN的模型。该模型取得了最先进的性能，超越了当前所有基于CNN的模型，甚至优于部分基于注意力机制的模型。其次，我们在小规模实验中将这些方法应用于现有的基于注意力机制的模型。在所有实验中，添加我们细节损失的注意力模型均较原始模型有所提升。这些结果表明，我们的方法能够有效增强不同模型结构下的超分辨率图像质量。