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

2025-01-01

IET Image Processing

AI Summary

This work addresses the limited ability of existing super-resolution methods to faithfully reconstruct high-frequency details. To this end, the authors propose a novel approach that integrates a Laplacian pyramid–based detail loss function with a recurrent upscaling–downscaling mechanism to explicitly disentangle and enhance high-frequency components during training. This design encourages the model to generate content and detail representations separately, thereby improving fine-texture recovery. The proposed method is architecture-agnostic and can be seamlessly incorporated into diverse network backbones. It achieves state-of-the-art performance when applied to CNN-based models and consistently yields significant gains across multiple attention-based architectures, demonstrating its effectiveness in enhancing the quality of high-frequency detail restoration.

Technology Category

• Computer Vision: Diffusion Models for Vision
• Machine Learning: Deep Neural Architectures and Foundation Models
• Natural Language Processing: Generation

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• super-resolution
• high-frequency details
• image enhancement
• detail loss
• Laplacian pyramid

Innovation

• Laplacian pyramid
• detail loss
• repeated upscaling and downscaling
• super-resolution
• high-frequency enhancement