Two-branch crisscross network for realistic and accurate image super-resolution

Convolutional Neural Networks have made remarkable progress in single-image super-resolution. However, existing methods struggle to balance reconstruction accuracy and perceptual quality, resulting in unsatisfactory outcomes. To address this challenge, we propose the Two-Branch Crisscross Generative Adversarial Network (TBCGAN) for achieving accurate and realistic super-resolution results. TBCGAN employs two asymmetric branches that separately reconstruct high-frequency (HF) and low-frequency (LF) images, leveraging their distinct information and reconstruction requirements. To ensure coherent results, we apply different supervision to the reconstructed HF, LF, and super-resolution (SR) images while facilitating information interaction through the interleaving and fusion of HF and LF features. Extensive experimental evaluations demonstrate that TBCGAN achieves an excellent balance between reconstruction accuracy and perceptual quality, outperforming GAN-based methods in reconstruction accuracy and MSE-based methods in perceptual quality.