Detail Loss in Super‐Resolution Models Based on the Laplacian Pyramid and Repeated Upscaling and Downscaling Process

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.