Parallel Alternating Iterative Optimization for Cardiac Magnetic Resonance Image Blind Super-Resolution

Cardiac magnetic resonance imaging (CMRI) super-resolution (SR) reconstruction technology can enhance the resolution and quality of CMRI, providing experts with clearer and more accurate information about cardiac structure and function. This technology aids in the rapid and accurate diagnosis of cardiac abnormalities and the development of personalized treatment plans. In the processing of CMRI, existing bicubic degradation-based SR methods often suffer from performance degradation, resulting in blurred SR images. To address the aforementioned problem, we present a parallel alternating iterative optimization for CMRI image blind SR method (PAIBSR). Specifically, we propose a parallel alternating iterative optimization strategy, which employs dynamically corrected blur kernels and dynamically extracted intermediate low-resolution features as prior knowledge for both the blind SR process and the blur kernel correction process. Meanwhile, we propose a blur kernel update module composed of a blur kernel extractor and a low-resolution kernel extractor to correct the blur kernel. Furthermore, we propose an enhanced spatial feature transformation residual block, leveraging the corrected blur kernel as prior knowledge for the blind SR process. Through extensive experiments conducted on synthetic datasets, we have validated the superiority of PAIBSR method. It outperforms state-of-the-art SR methods in terms of performance and produces visually pleasing results.