UCAN: Unified Convolutional Attention Network for Expansive Receptive Fields in Lightweight Super-Resolution

2026-03-12

AI Summary

This work addresses the high computational cost of existing hybrid CNN-Transformer super-resolution models when scaling receptive fields, which hinders deployment on resource-constrained devices. The authors propose UCAN, a lightweight architecture that unifies convolution and attention mechanisms to efficiently model both local textures and long-range dependencies. Key innovations include an integrated window-based spatial attention combined with Hedgehog Attention, a distilled large-kernel convolution module, and a cross-layer parameter sharing strategy, collectively reducing computational complexity. UCAN achieves a PSNR of 31.63 dB on Manga109 (4×) with only 48.4G MACs and 27.79 dB on BSDS100, demonstrating superior accuracy-efficiency trade-offs compared to most existing methods while using a smaller model footprint.

Technology Category

• Computer Vision: Large Vision Models
• Machine Learning: Large Multimodal Models (LMMs)
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Web Mining and Content Analysis: Large pretrained models with web data
• Responsible Web: Algorithmic accountability and transparency on the web

Abstract

Hybrid CNN-Transformer architectures achieve strong results in image super-resolution, but scaling attention windows or convolution kernels significantly increases computational cost, limiting deployment on resource-constrained devices. We present UCAN, a lightweight network that unifies convolution and attention to expand the effective receptive field efficiently. UCAN combines window-based spatial attention with a Hedgehog Attention mechanism to model both local texture and long-range dependencies, and introduces a distillation-based large-kernel module to preserve high-frequency structure without heavy computation. In addition, we employ cross-layer parameter sharing to further reduce complexity. On Manga109 ($4\times$), UCAN-L achieves 31.63 dB PSNR with only 48.4G MACs, surpassing recent lightweight models. On BSDS100, UCAN attains 27.79 dB, outperforming methods with significantly larger models. Extensive experiments show that UCAN achieves a superior trade-off between accuracy, efficiency, and scalability, making it well-suited for practical high-resolution image restoration.

Problem

• super-resolution
• lightweight
• receptive field
• computational cost
• resource-constrained devices

Innovation

• Unified Convolutional Attention
• Hedgehog Attention
• Large-Kernel Distillation
• Cross-Layer Parameter Sharing
• Lightweight Super-Resolution