RFA-GAN: A Dual-Attention Image-to-Image Translation With Robustness and Frequency Sensing Capabilities

In the field of unpaired image-to-image (I2I) translation, enforcing consistency through similarity constraints on features from corresponding stages of the encoder and decoder has led to state-of-the-art performance. However, this simple similarity constraint, typically based on mean-squared error, is limited in terms of feature alignment, spatial information utilization, and global context modeling. These limitations result in suboptimal detail preservation and global structure in the generated images. To address these issues, we propose RFA-GAN, an I2I translation framework that integrates noise injection and attention mechanisms. To enhance the generator’s ability to preserve fine details, we propose a channel shuffle dual attention module. Although generative adversarial networks have achieved remarkable progress in generating high-quality samples, the presence of noise and distortion still impairs translation performance. To mitigate this, we inject Gaussian noise into the model, which not only improves performance under noisy conditions but also reduces sensitivity to noise. Furthermore, to compensate for the insufficient capture of local details and the lack of global style information, we design a focal-frequency residual block. Extensive experiments on multiple benchmark datasets demonstrate that our method significantly improves the quality and detail preservation of generated images while enhancing model robustness. These results suggest that our approach provides an efficient and stable solution for unpaired I2I translation.