Comparative Analysis of ML-CSC Based CS-MRI Framework With State of the Art

Deep neural networks have been used extensively for inverse problems in image processing research. Subsequently the interest has been shown to theoretically model deep nets with celebrated sparse coding theory resulting in emergence of convolutional sparse coding (CSC) theory. The CSC theory which is a peculiar case of sparse coding works on the premise of representing underlying data (natural or biomedical images) with learned filters/dictionaries and their corresponding sparse feature maps. The dictionaries have special structure unlike their counter part of sparse coding and pursuit algorithms works on global scale instead of patches. This global pursuit results in mitigating the effects of patch aggregation process during traditional regularization-based techniques. Further extending the CSC model, the learning features are again processed through the CSC model representing them with another layer of filters/dictionaries and their corresponding sparse maps. This process is continued until the last layers of model making the multi-layer convolutional sparse coding model. The pursuit algorithms can be employed layer wise of a global pursuit settings utilizing iterative thresholding algorithms. In this work we have implemented a ML-CSC model for the reconstruction of Knee MR images on different CS ratios. The results have been compared with state of the art ISTA-Net+ model in terms of PSNR/SSIM and restoration times for different CS ratios. It is shown that ML-CSC has better quality results than the state of the art ISTA-Net+model.