Hybrid CNN-RWKV with high-frequency enhancement for real-world chinese-english scene text image super-resolution

Abstract

Existing scene text image super-resolution (STISR) methods primarily focus on the restoration of fixed-size English text images. Compared to English characters, Chinese characters present a greater variety of categories and more intricate stroke structures. In recent years, Transformer-based methods have achieved significant progress in image super-resolution task, but face the dilemma between global modeling and efficient computation. The emerging Receptance Weighted Key Value (RWKV) model can serve as a promising alternative to Transformer, enabling long-distance modeling with linear computational complexity. In this paper, we propose a Hybrid CNN-RWKV with High-Frequency Enhancement (HCR-HFE) model for STISR task. First, we design a recurrent bidirectional WKV (Re-Bi-WKV) attention which integrates bidirectional WKV (Bi-WKV) attention with a recurrent mechanism. Bi-WKV achieves global receptive field with linear complexity, while the recurrent mechanism establishes 2D image dependencies from different scanning directions. Additionally, a computationally efficient high-frequency enhancement module (HFEM) is incorporated to enhance high-frequency details, such as character edge information. Furthermore, we design a multi-scale large kernel convolutional (MLKC) block which integrates large kernel decomposition, gated aggregation and multi-scale mechanism to capture various-range dependencies with reduced computational cost. Finally, we introduce a multi-frequency channel attention (MFCA) which extends channel attention to the frequency domain, enabling the model to focus on critical features. Extensive experiments on real-world Chinese-English (Real-CE) dataset demonstrate that HCR-HFE outperforms previous methods in both quantitative metrics and visual results. Furthermore, HCR-HFE achieves excellent performance on natural image datasets, demonstrating its broad applicability.

Yanbin Liu 刘燕滨

Master.

A master student of this laboratory, research interests include Computer Vision, Text Image Analysis and Super-resolution Reconstruction.

Yu Zhu 朱煜

Professor. Experts in artificial intelligence and computer vision. Lab leader.

Leader of this laboratory, research interests include Artificial Intelligence, Computer Vision, Industrial controls, Digital Image and Video Processing, Machine learning, Deep Learning and Applications.

Hangyu Li 李航宇

PhD. Super listener of 《三国恋》🎵.

A doctoral student of this laboratory, research interests include Neural Radiance Fields, Medical Image Processing and Generative Model.