Reconstruction Kernel

Reconstruction kernels are mathematical functions used to generate images from raw data, impacting image quality and subsequent analysis. Current research focuses on improving kernel estimation and adaptation, particularly within deep learning frameworks employing iterative refinement and multi-attention mechanisms for tasks like image super-resolution and deblurring, as well as addressing inconsistencies across different imaging modalities and vendors. This work is crucial for enhancing image quality in various applications, from medical imaging (e.g., improving CT scan analysis) to computer vision (e.g., improving 3D rendering), where consistent and accurate kernels are essential for reliable results.