MCFINet: A Cost-Efficient Multi-Channel Feature Integration Network for Surface Scenarios Image Super-Resolution

作者：Liangcheng Zhao, Yueying Wang, Yuhao Qing, Dan Zeng, Li Xu · 发表于：ACM Transactions on Multimedia Computing Communications and Applications · 年份：2025 · DOI：10.1145/3777465 · 被引用次数：2 · 研究领域：Advanced Image Processing Techniques、Image Enhancement Techniques、Advanced Image Fusion Techniques

Convolutional Neural Network (CNN) and Vision Transformer (ViT) have revolutionized the field of image super-resolution (SR). However, their complexity poses challenges for resource—constrained scenarios, particularly due to the high computational demands of Transformers and their excessive reliance on global information. To tackle these challenges, we propose a Multi-Channel Feature Integration Network (MCFINet), designed to maximize input pixel utilization while minimizing computational overhead. It integrates both local and global features within the channels, thereby exploiting their complementary advantages. First, the designed Feature Integration Block (FIB) effectively captures local information and improves visual quality by enhancing the mapping of non-local features. Subsequently, we utilize the Adaptive Channel Fusion Block (ACFB), which strengthens the interaction between features and channels while maintaining computational efficiency. Finally, for SR task on resource-constrained surface scenarios, we propose a more suitable pre-training method, which further boosts the model’s learning ability. Evaluation results indicate that the proposed MCFINet achieves a better balance between lightweight design and high-quality restoration on both standard evaluation datasets and water surface target datasets. Specifically, compared to the traditional SwinIR-L, MCFINet reduces model training time and runtime by 12% on the test set, while also decreasing model complexity by ...