ABSR: Progressive Alternate Refinement for Blind Cardiac MRI Super-Resolution

Deep learning-based methods for super-resolution (SR) reconstruction of cardiac magnetic resonance imaging (CMRI) have achieved commendable reconstruction performance owing to the potent learning capability of neural networks. Nonetheless, these methods suffer from performance degradation when handling real-world CMRI images, failing to reconstruct high-fidelity CMRI high-resolution images. This degradation stems from the fact that real-world CMRI images are afflicted with blur and noise, whereas mainstream deep learning-based CMRI SR algorithms are typically trained on images degraded using bicubic methods. To address this problem, we propose a progressive alternate refinement for blind CMRI SR, which implements blind SR reconstruction through progressive alternate refinement optimization of the CMRI image feature extraction process and blur kernel feature extraction process. Moreover, we propose a novel progressive blind SR reconstruction subnetwork, which utilizes the alternate residual attention block (ARAB) to perform deep feature extraction. Meanwhile, we propose an ARAB, which uses channel and pixel attention mechanisms to extract high-frequency features from the extracted CMRI and blur kernel features. Extensive experimental results demonstrate that our proposed alternate refinement for blind CMRI super-resolution outperforms the state-of-the-art SR methods, exhibiting superior reconstruction performance and the ability to reconstruct high-fidelity CMRI images.