A Practical CNN–Transformer Hybrid Network for Real-World Image Denoising

Real-world image denoising faces a critical trade-off: Convolutional Neural Network (CNN)-based methods are computationally efficient but limited in capturing long-range dependencies, while Transformer-based approaches achieve superior global modeling at prohibitive computational costs (>100 G Multiply–Accumulate Operations, MACs). This presents significant challenges for deployment in resource-constrained environments. We present a practical CNN–Transformer hybrid network that systematically balances performance and efficiency under practical deployment constraints for real-world image denoising. By integrating key components from NAFNet (Nonlinear Activation Free Network) and Restormer, our method employs three design strategies: (1) strategic combination of CNN and Transformer blocks enabling performance–efficiency trade-offs; (2) elimination of nonlinear operations for hardware compatibility; and (3) architecture search under explicit resource constraints. Experimental results demonstrate competitive performance with significantly reduced computational cost: our models achieve 39.98–40.05 dB Peak Signal-to-Noise Ratio (PSNR) and 0.958–0.961 Structural Similarity Index Measure (SSIM) on the SIDD dataset, and 39.73–39.91 dB PSNR and 0.959–0.961 SSIM on the DND dataset, while requiring 7.18–16.02 M parameters and 20.44–44.49 G MACs. Cross-validation results show robust generalization without significant performance degradation across diverse scenes, demonstrating a favorable trade-off among performance, efficiency, and practicality.