Integrating Classical Image Filters with Physics-Driven Deep Learning for Sharper Image Reconstruction

Motivation: To improve the sharpness of physics-driven deep learning (PD-DL) reconstruction by incorporating Laplacian filter blocks into existing unrolled networks. Goal(s): This study aims to integrate traditional Laplacian filtering to refine PD-DL reconstruction, specifically targeting blurring artifacts. Approach: Laplacian sharpening filters with a single tunable weight are incorporated to the output of regularization units in unrolled PD-DL networks. These networks are compared to conventional unrolled networks with matching architectures under same supervised learning conditions. Results: The proposed approach improves image clarity and detail in reconstructed MR images, indicating that integrating a classic building block with deep learning may enhance overall performance. Impact: The proposed approach incorporates simple Laplacian sharpening filters into unrolled networks, which is shown to enhance sharpness, with visual improvements. This hybrid methodology represents a promising direction, merging traditional techniques with DL for superior image quality.